Overview
This study examines the link between maternal folic acid supplementation before and during pregnancy and the risk of delivering a low birth weight (LBW) infant, defined as a birth weight below 2,500 grams.1 LBW is associated with higher neonatal morbidity and long-term health issues. Folic acid, vital for fetal development, may help prevent LBW by supporting placental health. Using data from the Pregnancy Risk Assessment Monitoring System (PRAMS), Phase 8, the study finds that folic acid supplementation during pregnancy appears to reduce LBW risk, while pre-pregnancy intake may slightly increase it. Socioeconomic factors, such as education and income, as well as race and prenatal care, also play significant roles in LBW outcomes. The study highlights the need for targeted interventions addressing these factors to reduce LBW rates. I consulted with Dr. John Holmes and Dr. Ellen Caniglia from the Department of Biostatistics, Epidemiology & Informatics. Dr. Holmes highlighted potential study limitations, such as selection bias, while Dr. Caniglia shared a relevant paper that examines the link between supplementation and adverse pregnancy outcomes like preterm birth (PTB) and LBW. You can access the final project, including all code and results, on my GitHub: https://github.com/msaolabisi/BMIN503_Final_Project.git
Introduction
The problem I’m addressing in this project is the potential link between maternal folic acid supplementation before and during pregnancy and the risk of delivering a LBW infant, defined as a birth weight under 2,500 grams. LBW is an important indicator of adverse pregnancy outcomes, including higher rates of neonatal morbidity, mortality, and long-term developmental issues like cardiovascular and metabolic disorders.2 These conditions put a lot of stress on healthcare systems, driving up costs and the need for more intensive care, including extended stays in the NICU (Neonatal Intensive Care Unit).3 It’s important to identify maternal factors that can be changed to lower the risk of LBW, as this could help improve the health of both mothers and babies.
Folic acid is a B vitamin that’s often recommended during pregnancy to help prevent birth defects like spina bifida.4 It helps with DNA production, cell growth, and tissue development, which are all crucial for the rapid development of the fetus.5 Folic acid may also support the placenta, which provides the fetus with nutrients and oxygen, and a healthy placenta can help prevent LBW.5 By reducing these issues, folic acid might help lower the chances of having an LBW baby.
Despite these potential benefits, the direct relationship between folic acid and LBW isn’t fully established, and the research results are inconsistent. This is why it’s important to further investigate how folic acid supplementation before and during pregnancy might affect LBW.
This issue spans multiple fields. Epidemiology helps us understand the patterns and causes of LBW, while biostatistics is used to analyze large datasets like PRAMS. Public health looks at how factors like folic acid impact maternal and fetal health at the population level. Nutrition explains the role of folic acid in fetal development, and obstetrics and maternal health offer insights into how maternal health, prenatal care, and pregnancy complications influence birth weight outcomes.
When I spoke with Dr. John Holmes and Dr. Ellen Caniglia from the Department of Biostatistics, Epidemiology & Informatics, they helped me better understand the challenges of studying this issue. Dr. Holmes stressed the importance of addressing biases, like selection bias, when interpreting data in this case and Dr. Caniglia shared her research on supplementation and pregnancy outcomes, providing valuable context for studying the link between folic acid and LBW.
By drawing from these different fields of expertise, this project seeks to clarify the potential role of folic acid supplementation in reducing LBW.
Methods
This study is a secondary analysis of data collected from the Phase 8 (2016–2022) Pregnancy Risk Assessment Monitoring System (PRAMS), a population-based surveillance system that collects data on maternal attitudes, behaviors, and experiences before, during, and after pregnancy conducted by the Centers for Disease Control and Prevention (CDC) in collaboration with state health departments. The dataset included responses from 249,970 women across all U.S. states who gave birth within the study period.
The PRAMS survey included a core set of questions and optional standard questions tailored to state-specific needs. For this study, the primary exposure variables were folic acid supplementation before and during pregnancy, both categorized as “Yes” or “No.” The primary outcome was infant birth weight, categorized as “Yes Low Birth Weight” or “No Low Birth Weight” Covariates included maternal age, race/ethnicity, education, smoking during pregnancy, pre-pregnancy BMI, prenatal care utilization, state, and income.
Descriptive statistics were used to summarize maternal and infant characteristics, stratified by LBW status, and analyzed using t-tests for continuous variables and chi-square tests for categorical variables. Univariate and multivariate logistic regression models were employed to estimate unadjusted and adjusted odds ratios (ORs) for the relationship between folic acid intake (before and during pregnancy) and LBW. Additional tables and figures included 2x2 tables to assess the relationship between exposures and outcome, bar plots displaying the relationship between exposures, covariates, and LBW, and a dot plot showing LBW counts/rates across states. A forest plot was also generated to display both adjusted and unadjusted odds ratios (ORs) for the association between folic acid intake (before and during pregnancy) and LBW.
Install, Read, & Load Packages {#sec-install-read-&-load-packages}
options (repos = c (CRAN = "https://cloud.r-project.org" )) ##install packages install.packages ("haven" )
The downloaded binary packages are in
/var/folders/pn/17723btn1rn2dbrjlxwg_y580000gp/T//RtmpjzROeC/downloaded_packages
install.packages ("tableone" )
The downloaded binary packages are in
/var/folders/pn/17723btn1rn2dbrjlxwg_y580000gp/T//RtmpjzROeC/downloaded_packages
install.packages ("viridis" )
The downloaded binary packages are in
/var/folders/pn/17723btn1rn2dbrjlxwg_y580000gp/T//RtmpjzROeC/downloaded_packages
The downloaded binary packages are in
/var/folders/pn/17723btn1rn2dbrjlxwg_y580000gp/T//RtmpjzROeC/downloaded_packages
The downloaded binary packages are in
/var/folders/pn/17723btn1rn2dbrjlxwg_y580000gp/T//RtmpjzROeC/downloaded_packages
The downloaded binary packages are in
/var/folders/pn/17723btn1rn2dbrjlxwg_y580000gp/T//RtmpjzROeC/downloaded_packages
##load packages library (gt)library (haven)library (dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library (tidyr)library (ggplot2)library (caret)
Loading required package: lattice
Attaching package: 'boot'
The following object is masked from 'package:lattice':
melanoma
library (tableone)library (broom)library (viridis)
Loading required package: viridisLite
Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
Attaching package: 'maps'
The following object is masked from 'package:viridis':
unemp
# read a .sas7bdat file <- read_sas ("/Users/molabisi/Downloads/PRAMS ARF/phase8_arf_2016_2022.sas7bdat" )# view the first few rows of the data head (data)
# A tibble: 6 × 486
ID STATE HISPANIC MODECONT PARTICIP MODE_PRT MM_DOB B_ORDER BRSTFED
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2016AK327002 AK 1 2 1 2 NA 1 2
2 2016AK327006 AK 1 2 1 2 NA 1 1
3 2016AK327007 AK 1 1 1 1 NA 1 1
4 2016AK327009 AK 1 2 1 2 NA 1 1
5 2016AK327010 AK 1 2 1 2 NA 1 1
6 2016AK327011 AK 1 2 1 2 NA 1 1
# ℹ 477 more variables: CIG_1TRI <dbl>, CIG_2TRI <dbl>, CIG_3TRI <dbl>,
# CIG_PRIOR <dbl>, DEFECT <dbl>, DEL_1CS <dbl>, DEL_FORC <dbl>,
# DEL_RCS <dbl>, DEL_VACM <dbl>, DEL_VAG <dbl>, DEL_VCS <dbl>,
# FHISP_BC <dbl>, FRACE_AMI <dbl>, FRACE_BLK <dbl>, FRACE_WHT <dbl>,
# HISP_BC <dbl>, INFER_TR <dbl>, MARRIED <dbl>, MAT_DEG <dbl>, MAT_ED <dbl>,
# MAT_HTFT <dbl>, MAT_HTIN <dbl>, MAT_PRWT <dbl>, MAT_TRAN <dbl>,
# MAT_WIC <dbl>, MM_DIAB <dbl>, MM_FEVER <dbl>, MM_HBP <dbl>, MM_LMP <dbl>, …
Cleaning Data
## count/how many people in data set cat ("Total rows in the dataset:" , nrow (data), " \n " )
Total rows in the dataset: 249970
##cleaning data <- data %>% ##recoding exposure variables mutate (# For PRE_VIT_RAW (pre preg -- PPV -- folic acid) PRE_VIT_RAW = case_when (== 1 ~ 0 , # NO -> 0 == 2 ~ 1 , # YES -> 1 TRUE ~ NA_real_ # Keep other values unchanged # For VPP_VIT_RAW (during preg -- PPV -- folic acid) VPP_VIT_RAW = case_when (== 1 ~ 0 , # NO -> 0 == 2 ~ 1 , # YES -> 1 TRUE ~ NA_real_ # Keep other values unchanged # Convert to factors after recoding PRE_VIT_RAW = factor (PRE_VIT_RAW, levels = c (0 , 1 )),VPP_VIT_RAW = factor (VPP_VIT_RAW, levels = c (0 , 1 ))## recoding outcome variables 4 categories <- data_clean %>% mutate (GRAM_NAPHSIS = case_when (< 1500 ~ 0 , # VLBW: Less than 1500 grams >= 1500 & GRAM_NAPHSIS < 2500 ~ 1 , # LBW: 1500-2499 grams >= 2500 & GRAM_NAPHSIS <= 4000 ~ 2 , # NBW: 2500-4000 grams > 4000 ~ 3 , # Macrosomia: Over 4000 grams TRUE ~ NA_real_ # Missing or invalid data GRAM_NAPHSIS = factor (GRAM_NAPHSIS, levels = c (0 , 1 , 2 , 3 )),## recoding outcome variable dichotomous LBW = case_when (== 0 | GRAM_NAPHSIS == 1 ~ 1 , # VLBW or LBW == 2 | GRAM_NAPHSIS == 3 ~ 0 , # NBW or Macrosomia TRUE ~ NA_real_ # Missing or invalid data LBW = factor (LBW, levels = c (0 , 1 )),table (data_clean$ PRE_VIT_RAW)table (data_clean$ VPP_VIT_RAW)table (data_clean$ GRAM_NAPHSIS)
0 1 2 3
8491 44367 180131 16702
## cleaning data <- data_clean %>% ##cleaning covariates mutate (# Maternal Age MAT_AGE_PU = as.numeric (MAT_AGE_PU),# Parity PRE_LB_NAPHSIS = case_when (== 0 ~ 0 , # No children (0) == 1 ~ 1 , # One child (1) %in% c (2 , 3 , 4 ) ~ 2 , # 2+ children (Yes, multiple) TRUE ~ NA_real_ PRE_LB_NAPHSIS = as.factor (PRE_LB_NAPHSIS),# BMI MOM_BMI = as.numeric (MOM_BMI),# Education MAT_ED = case_when (%in% c (1 , 2 ) ~ "<= 12th grade, no diploma" , == 3 ~ "High school graduate / GED" , == 4 ~ "Some college, no degree" , == 5 ~ "Bachelor's, Master's, or higher" , TRUE ~ NA_character_ MAT_ED = as.factor (MAT_ED),# Income INCOME8 = case_when (%in% c (1 , 2 , 3 , 4 , 5 , 6 , 13 , 14 , 101 , 102 , 103 , 104 , 105 ) ~ "Low income (0 - $40,000)" , %in% c (7 , 8 , 9 , 10 , 11 , 106 , 107 , 108 , 109 ) ~ "Middle ($40,001 - $85,000)" ,%in% c (12 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 110 , 111 , 112 ) ~ "High income ($85,001 or more)" , TRUE ~ NA_character_ INCOME8 = as.factor (INCOME8),STATE = as.factor (STATE), PNC_VST = as.factor (PNC_VST),MAT_RACE_PU = case_when (== 2 ~ "White" ,== 3 ~ "Black" ,== 4 ~ "American Indian" ,== 10 ~ "Alaska Native" ,== 21 ~ "Asian" ,== 22 ~ "Hawaiian/Other Pacific Islander" ,== 23 ~ "Other/Multiple Race" ,TRUE ~ NA_character_ MAT_RACE_PU = as.factor (MAT_RACE_PU)table (data_clean$ PRE_VIT_RAW)table (data_clean$ VPP_VIT_RAW)table (data_clean$ GRAM_NAPHSIS)
0 1 2 3
8491 44367 180131 16702
table (data_clean$ MAT_AGE_PU)
17 18 19 20 21 22 23 24 25 26 27 28 29
2822 2903 4871 6291 7528 8549 9464 10531 11728 12747 13511 14748 15113
30 31 32 33 34 35 36 37 38 39 40 41 42
15463 15462 14615 13416 12348 11007 9337 7351 5779 4375 3147 2181 1429
43 44 45
808 444 607
table (data_clean$ PRE_LB_NAPHSIS)table (data_clean$ MOM_BMI)
13.0441819325197 13.0634954258731 13.1430886680299 13.1627654046335
1 1 1 1
13.1690585961817 13.1827763655527 13.2007121157099 13.20712700597
1 1 1 1
13.266879414823 13.2896715497573 13.2907638216033 13.3127840220177
1 2 1 3
13.3129693748806 13.3893701629895 13.457366002741 13.479193822293
2 1 1 1
13.4876841182254 13.5186298252744 13.528842768854 13.534865779409
1 2 1 1
13.5625271250543 13.5633680555556 13.5739916450247 13.6230741211482
1 1 1 1
13.6311701194831 13.6621487603306 13.6683153747184 13.6710273420547
1 1 4 1
13.6736253308675 13.6845429400201 13.7142857142857 13.7194489265012
1 1 1 4
13.7320587141174 13.7344256302991 13.7463322494503 13.7548820804013
4 1 1 1
13.7746561207408 13.8077879805829 13.8115669414676 13.86
2 1 2 1
13.9006729626362 13.9037094480439 13.9394620513456 13.9472605864474
1 1 2 1
13.9784232231186 13.9851058459858 13.9953719008264 13.9983313207624
1 1 1 1
14.0291395894701 14.0316478286734 14.0589850340698 14.0625
1 1 1 1
14.0753601819704 14.0819060679942 14.0867331923119 14.0960852204618
1 1 1 5
14.117040806797 14.1712188826151 14.1714569143424 14.175
1 2 5 1
14.1768147430435 14.2036647709659 14.2300166536579 14.2332699725087
3 1 3 3
14.240653481307 14.2470109158968 14.2664801458635 14.2927448484655
1 1 1 1
14.3244898966168 14.3650700524542 14.3656784040408 14.4186616498233
1 1 1 3
14.4447953255768 14.4493837374772 14.4721650501902 14.4776526239443
1 1 5 1
14.5051510099053 14.5128456892305 14.525743337201 14.5264753339424
3 1 4 9
14.5423330203167 14.5608575240819 14.5659547278105 14.5891017218214
1 1 2 1
14.5903123837498 14.5943737960926 14.5968458026881 14.6240932481865
5 1 3 1
14.6322873290908 14.6440624242195 14.6446236157697 14.6451823107139
17 1 5 3
14.6503128545009 14.6878806154307 14.692378328742 14.7028865486302
1 1 1 2
14.7225778969268 14.7488962797995 14.7583302979106 14.7619631176762
1 2 1 1
14.7652231023965 14.767515357337 14.792899408284 14.8104722244947
1 5 1 1
14.8151909207044 14.8229340142269 14.8492858969189 14.879201066043
2 1 10 8
14.8800297600595 14.9009467569108 14.919384677479 14.9225441308025
2 3 1 1
14.9270172230213 14.9336138516027 14.97688202477 14.9980945123181
1 1 14 3
14.99906999814 15 15.0106911784072 15.0231069692909
1 1 2 2
15.0277308864867 15.0358242351593 15.0529972340222 15.0551394482291
1 2 2 1
15.0571729714888 15.0601009584546 15.0628656644837 15.0660301320603
5 1 1 2
15.0958033048897 15.1159668081228 15.136749023498 15.1432918250452
2 12 2 2
15.1479527335786 15.1601928203856 15.1616305612147 15.1720964598954
1 1 1 3
15.1725204932864 15.1809981039317 15.1924474042755 15.1927236356143
2 5 3 1
15.2025140392276 15.2050477111335 15.209516133318 15.2105408180571
4 14 2 3
15.2334304668609 15.2343161829181 15.234375 15.2769153194556
2 2 1 3
15.3044554540976 15.3049163932243 15.309 15.3097016253204
2 3 1 1
15.3226112903839 15.3256556513113 15.3335017413836 15.3419866450921
1 1 18 8
15.3490705733918 15.3502404103343 15.3570981882448 15.3582159716304
12 2 1 2
15.3639016955453 15.4071893242399 15.4114593943474 15.415851374796
6 2 4 6
15.4485660533821 15.4512531822582 15.4612809225618 15.4661189227254
25 1 1 2
15.4761114255956 15.4814592509566 15.4938659783259 15.4949070228719
4 1 4 7
15.505693742508 15.5058911252038 15.5124653739612 15.5157644259369
5 7 1 1
15.5344348469098 15.54 15.546805287159 15.5522172578387
1 1 18 1
15.5817174515235 15.5886026057766 15.5969061938124 15.600918775802
1 7 1 1
15.6209318568211 15.6210944822998 15.6226185032769 15.6240312480625
3 2 1 16
15.625 15.6270003781343 15.6399741370911 15.6425212258709
1 1 1 8
15.6530183190237 15.6535662787772 15.6563123043601 15.6611991424675
1 1 7 1
15.6623169116242 15.6751205297892 15.6828155634274 15.6856444594994
26 1 3 2
15.6955017301038 15.7258379032887 15.7297088787726 15.7541941053431
2 1 10 1
15.7657458172059 15.7716017911375 15.7717347961535 15.775179479353
6 1 3 1
15.7834601383488 15.791867521235 15.7943067033976 15.7971507078748
5 11 1 1
15.8012414323505 15.8089310261461 15.8132496195788 15.8177175858484
2 23 3 20
15.8189400807405 15.8193316386633 15.8242006234705 15.8291098630034
2 1 2 1
15.8321952973424 15.871765148529 15.893586474673 15.8977916522141
1 4 2 1
15.89987706855 15.9012345679012 15.9126124703862 15.9264579143924
2 1 8 1
15.9428890286352 15.9476058597088 15.9489165859239 15.9561709528475
41 3 4 1
15.9635182551615 15.9653000966902 15.9753408264213 15.9755632498567
7 11 4 4
15.9791228673366 15.9889230708832 16.0087687353651 16.0116218443573
6 1 3 1
16.0124287190723 16.0146320292641 16.015625 16.0230732254446
3 4 1 1
16.0299085320951 16.0393496744145 16.0491555460832 16.0503279586441
1 8 1 1
16.0607147336305 16.0676498638316 16.0704321408643 16.0714285714286
20 1 1 2
16.089997577919 16.0955160619999 16.0960652189647 16.0965917394973
1 5 1 2
16.0993788819876 16.1173505738015 16.1200322400645 16.1290645161935
1 5 3 2
16.132186418973 16.1326853265951 16.135168989088 16.1353938828929
12 1 11 1
16.1405281488249 16.1417506266965 16.1581478003519 16.1616161616162
58 10 28 1
16.1787305823132 16.178822280279 16.2084992748161 16.2099324198648
1 2 1 6
16.213918142122 16.2280001797759 16.2442668930706 16.245665385714
2 1 32 2
16.248992497985 16.249664318732 16.2692386616528 16.2694010509128
6 11 1 1
16.2720052002604 16.2750325500651 16.2781253938899 16.2784196536135
2 8 3 12
16.2888095880892 16.2964023607079 16.2971754514938 16.3019334303131
11 1 12 4
16.3021253509808 16.3068197230144 16.3081604269717 16.3182948861434
2 45 31 3
16.3279338842975 16.3350110388732 16.3531485338888 16.3574041433797
1 2 1 2
16.3663720184583 16.368260468385 16.3846974546563 16.391942046644
1 3 1 5
16.4052328104656 16.40625 16.4214515280242 16.425889698231
5 2 7 1
16.4365548980934 16.4386139038336 16.4454327572055 16.446348682171
1 11 28 1
16.4490124898617 16.4610248221803 16.4613232452271 16.4633387118014
1 2 51 16
16.4699266824744 16.474318662923 16.474570227247 16.4751837690978
1 23 18 1
16.4784704569409 16.480276816609 16.498976997954 16.5008901446374
25 1 1 7
16.5289256198347 16.5371463192248 16.5396172002174 16.5545483445538
2 1 10 1
16.5603522902631 16.5606533930521 16.5621014953607 16.5621323380259
1 1 6 1
16.5630070308275 16.5735020051355 16.5887573964497 16.5888661702866
2 5 1 1
16.5972400978724 16.6005332010664 16.6016860959342 16.6020559263217
1 24 1 10
16.6238050622095 16.6247439932896 16.6275634889351 16.6409800275222
1 14 8 88
16.6442268368408 16.6493236212279 16.6501211908674 16.6514618743523
2 1 23 19
16.6530612244898 16.6611570247934 16.6622209084246 16.6714557786762
1 5 2 1
16.6819083638167 16.6911217679265 16.7062046844198 16.7165518346419
2 3 1 1
16.7201285651085 16.7240922176978 16.7255524822468 16.7311124330756
11 3 55 1
16.7367127037369 16.748961497923 16.7586790954379 16.7640783428651
13 1 18 8
16.765736930346 16.7751479289941 16.7861492747779 16.7878621471529
3 1 11 2
16.7926765684723 16.7958335916672 16.8 16.8061342389972
2 4 1 1
16.8073898277974 16.8147657723887 16.8165130740366 16.8175336350673
9 2 14 2
16.8217719247939 16.8242268867169 16.8271304284544 16.8277685950413
79 3 28 1
16.8286050857816 16.8349675073641 16.8401342975207 16.8658734451055
61 3 1 2
16.8662506324844 16.8739537479075 16.8757733965172 16.8761853096013
2 1 2 3
16.8776029593582 16.8791473317019 16.8876749838319 16.8962350780533
6 2 1 4
16.898287281593 16.9153022645542 16.9191695032978 16.9204590061004
3 19 1 2
16.9292504096455 16.9301797233153 16.9313481484106 16.9355177420032
1 1 2 1
16.9370318792578 16.9382716049383 16.9475545562661 16.9531589063178
2 1 67 3
16.9550173010381 16.9660551903695 16.9737996280726 16.9810080350621
2 5 14 1
16.9826426609375 16.9832333820986 16.991133982268 16.9922996878252
44 1 9 3
16.9934226587203 17.0054626591382 17.0057482972109 17.01
23 62 29 1
17.010034020068 17.0156579154658 17.0277528055056 17.0288426514353
14 1 1 5
17.0296534364695 17.0357142857143 17.0392308004382 17.0481910523869
18 1 2 1
17.0519031141869 17.056176513282 17.0719254336704 17.0725281342918
2 1 11 1
17.0748341496683 17.0760199843894 17.0763015010325 17.0799239670105
1 1 1 4
17.079955135395 17.0816668163949 17.0874449997864 17.0887841775684
1 1 2 2
17.1029149315883 17.1089598377544 17.1111111111111 17.1303178145109
1 22 1 5
17.136 17.1381317792362 17.1402094283478 17.1457133037723
1 3 21 2
17.1467764060357 17.1515449054542 17.1551305213303 17.156262035643
2 4 1 1
17.1609917355372 17.1650733926468 17.1655066945476 17.1680320378739
3 199 1 25
17.1817039135808 17.1828915086402 17.1832039305298 17.1864343728687
3 30 4 13
17.1875 17.1929376116817 17.1944122442397 17.1946034565033
2 33 10 2
17.1964285714286 17.2110868084191 17.218320150926 17.224803717353
1 2 26 2
17.2285486027867 17.2426380355719 17.2548129175233 17.2616567857715
121 2 1 1
17.2635445362718 17.2644586768345 17.2703651192426 17.2779929680842
6 4 19 12
17.2839506172839 17.2879068982965 17.2946031271948 17.3066192286231
3 1 4 15
17.3119459812327 17.3134103654635 17.3330879897698 17.3337543906922
7 1 1 11
17.3343195266272 17.3343541953356 17.3367241265733 17.3387443549081
1 3 22 3
17.3471333863001 17.3571428571429 17.3600347200694 17.3604413409976
1 1 116 2
17.3611111111111 17.364375 17.3699063475546 17.3700088853783
1 1 1 24
17.3758412032953 17.3817347634695 17.3833618293412 17.3851717719029
104 21 23 8
17.3979939796614 17.402990426333 17.4040583837665 17.4072613770228
1 1 1 6
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62.000124000248 62.1872211486358 62.2222222222222 62.237265302933
6 2 1 1
62.3008246016492 62.3092164153078 62.3360213082645 62.3533630835066
1 1 1 1
62.4036751032082 62.4349635796046 62.4808671492343 62.4833284065923
1 1 1 1
62.49612499225 62.5739948525109 62.6128227263637 62.649267646497
3 1 1 11
62.6525178831608 62.7086968459651 62.7866544989288 62.8623132246265
1 1 1 1
62.9480597804171 62.9625223081499 63.0629832688237 63.0693143043091
2 1 1 1
63.074586811331 63.101739106704 63.1674700849402 63.2357241045843
1 1 1 3
63.3380513776638 63.4726269452539 63.5107715527931 63.5944129031115
1 1 2 1
63.6824222867196 63.7715561145408 64.0162570647722 64.0497148762893
2 3 5 1
64.0585281170562 64.2393020323231 64.242858934522 64.3311833498667
1 1 2 2
64.3690252224254 64.3820642479995 64.4007073138113 64.4318085196235
3 2 1 1
64.4370147394868 64.4491288982578 64.5162580647742 64.5406759563519
1 1 1 1
64.5621125952996 64.5649678396131 64.5670025067861 64.7478714312267
6 1 1 1
64.8283418553689 64.8998221073365 65.0350300700601 65.2272788920578
1 3 1 2
65.5429882288336 65.6085968256593 65.60872496745 65.7544556153343
2 1 1 1
65.7894736842105 65.8452929809085 65.8972746516922 66.0356350298499
1 1 1 4
66.0744178631215 66.1323547855373 66.2264300307148 66.2515610745507
1 2 1 2
66.5639201100887 66.6551246537396 66.7927335854672 66.9427145305904
4 1 1 1
66.988033976068 67.1345092690186 67.3144203431264 67.4028455494928
1 1 1 1
67.4819946791197 68.0218506365527 68.2622268470343 68.3169921027342
1 2 1 1
68.3551367102734 68.9087092459899 68.9665985620603 69.0858524574192
3 1 1 2
69.1075434629003 69.137557629954 69.533447317365 69.7222394444789
1 1 1 1
<= 12th grade, no diploma Bachelor's, Master's, or higher
29383 88853
High school graduate / GED Some college, no degree
59994 69420
AK AL AR AZ CO DC DE GA HI IA IL IN KS
5338 4602 4546 1640 8520 2554 6166 3993 5619 4436 7471 865 6339
KY LA MA MD ME MI MN MO MP MS MT ND NE
3940 5081 10115 5217 5712 11077 3597 7347 216 4371 5331 4107 8172
NH NJ NM NY OR PA PR RI SD TN UT VA VT
3039 7888 7818 5064 7627 7844 5830 5118 6419 1929 9824 6759 6047
WA WI WV WY YC
8344 7807 3389 3515 9337
table (data_clean$ INCOME8)
High income ($85,001 or more) Low income (0 - $40,000)
60973 107957
Middle ($40,001 - $85,000)
53080
table (data_clean$ PNC_VST)
0 1 2 3 4 5 6 7 8 9 10 11 12
3504 1253 2039 2846 4105 5992 8130 10222 15324 18055 32468 27039 36332
13 14 15 16 17 18 19 20 21 22 23 24 25
23637 17898 12546 6572 3795 2877 1679 2350 858 670 477 387 413
26 27 28 29 30 31 32 33 34 35 36 37 38
203 154 153 89 226 39 52 40 32 40 32 24 25
39 40 41 42 43 44 45 46 47 48 49 50 51
30 60 12 7 5 2 3 3 5 5 8 10 1
53 54 56 58 60 64 66 88 90 94
1 1 1 3 4 1 1 1 1 1
table (data_clean$ MAT_RACE_PU)
American Indian Asian
8693 16210
Black Hawaiian/Other Pacific Islander
43561 1326
Other/Multiple Race White
25898 140667
Descriptive Statistics
#specifying continuous and categorical variables # List of continuous variables <- c ("MAT_AGE_PU" , "MOM_BMI" )# List of categorical variables <- c ("PRE_VIT_RAW" , "VPP_VIT_RAW" , "GRAM_NAPHSIS" , "LBW" , "PRE_LB_NAPHSIS" , "MAT_ED" , "STATE" , "INCOME8" , "PNC_VST" , "MAT_RACE_PU" )
Descriptive Table - ALL
# descriptive tables <- CreateTableOne (vars = c (cont_vars, cat_vars), data = data_clean)print (table1, showAllLevels = TRUE )
level Overall
n 249970
MAT_AGE_PU (mean (SD)) 29.30 (5.76)
MOM_BMI (mean (SD)) 27.11 (6.89)
PRE_VIT_RAW (%) 0 110466 (63.8)
1 62669 (36.2)
VPP_VIT_RAW (%) 0 96628 (44.4)
1 121177 (55.6)
GRAM_NAPHSIS (%) 0 8491 ( 3.4)
1 44367 (17.8)
2 180131 (72.1)
3 16702 ( 6.7)
LBW (%) 0 196833 (78.8)
1 52858 (21.2)
PRE_LB_NAPHSIS (%) 0 98325 (39.4)
1 78077 (31.3)
2 73021 (29.3)
MAT_ED (%) <= 12th grade, no diploma 29383 (11.9)
Bachelor's, Master's, or higher 88853 (35.9)
High school graduate / GED 59994 (24.2)
Some college, no degree 69420 (28.0)
STATE (%) AK 5338 ( 2.1)
AL 4602 ( 1.8)
AR 4546 ( 1.8)
AZ 1640 ( 0.7)
CO 8520 ( 3.4)
DC 2554 ( 1.0)
DE 6166 ( 2.5)
GA 3993 ( 1.6)
HI 5619 ( 2.2)
IA 4436 ( 1.8)
IL 7471 ( 3.0)
IN 865 ( 0.3)
KS 6339 ( 2.5)
KY 3940 ( 1.6)
LA 5081 ( 2.0)
MA 10115 ( 4.0)
MD 5217 ( 2.1)
ME 5712 ( 2.3)
MI 11077 ( 4.4)
MN 3597 ( 1.4)
MO 7347 ( 2.9)
MP 216 ( 0.1)
MS 4371 ( 1.7)
MT 5331 ( 2.1)
ND 4107 ( 1.6)
NE 8172 ( 3.3)
NH 3039 ( 1.2)
NJ 7888 ( 3.2)
NM 7818 ( 3.1)
NY 5064 ( 2.0)
OR 7627 ( 3.1)
PA 7844 ( 3.1)
PR 5830 ( 2.3)
RI 5118 ( 2.0)
SD 6419 ( 2.6)
TN 1929 ( 0.8)
UT 9824 ( 3.9)
VA 6759 ( 2.7)
VT 6047 ( 2.4)
WA 8344 ( 3.3)
WI 7807 ( 3.1)
WV 3389 ( 1.4)
WY 3515 ( 1.4)
YC 9337 ( 3.7)
INCOME8 (%) High income ($85,001 or more) 60973 (27.5)
Low income (0 - $40,000) 107957 (48.6)
Middle ($40,001 - $85,000) 53080 (23.9)
PNC_VST (%) 0 3504 ( 1.4)
1 1253 ( 0.5)
2 2039 ( 0.8)
3 2846 ( 1.2)
4 4105 ( 1.7)
5 5992 ( 2.5)
6 8130 ( 3.3)
7 10222 ( 4.2)
8 15324 ( 6.3)
9 18055 ( 7.4)
10 32468 (13.4)
11 27039 (11.1)
12 36332 (15.0)
13 23637 ( 9.7)
14 17898 ( 7.4)
15 12546 ( 5.2)
16 6572 ( 2.7)
17 3795 ( 1.6)
18 2877 ( 1.2)
19 1679 ( 0.7)
20 2350 ( 1.0)
21 858 ( 0.4)
22 670 ( 0.3)
23 477 ( 0.2)
24 387 ( 0.2)
25 413 ( 0.2)
26 203 ( 0.1)
27 154 ( 0.1)
28 153 ( 0.1)
29 89 ( 0.0)
30 226 ( 0.1)
31 39 ( 0.0)
32 52 ( 0.0)
33 40 ( 0.0)
34 32 ( 0.0)
35 40 ( 0.0)
36 32 ( 0.0)
37 24 ( 0.0)
38 25 ( 0.0)
39 30 ( 0.0)
40 60 ( 0.0)
41 12 ( 0.0)
42 7 ( 0.0)
43 5 ( 0.0)
44 2 ( 0.0)
45 3 ( 0.0)
46 3 ( 0.0)
47 5 ( 0.0)
48 5 ( 0.0)
49 8 ( 0.0)
50 10 ( 0.0)
51 1 ( 0.0)
53 1 ( 0.0)
54 1 ( 0.0)
56 1 ( 0.0)
58 3 ( 0.0)
60 4 ( 0.0)
64 1 ( 0.0)
66 1 ( 0.0)
88 1 ( 0.0)
90 1 ( 0.0)
94 1 ( 0.0)
MAT_RACE_PU (%) American Indian 8693 ( 3.7)
Asian 16210 ( 6.9)
Black 43561 (18.4)
Hawaiian/Other Pacific Islander 1326 ( 0.6)
Other/Multiple Race 25898 (11.0)
White 140667 (59.5)
#summary(table1, showAllLevels = TRUE)
The sample consists mostly of low-income, white women, with about 36% reporting pre-pregnancy folic intake and about 56% reporting pregnancy folic intake. The population had an average count of prenatal care visits. The population had a higher BMI, were around the average age to have a baby, most were nulliparous, with about 1/5 having a low birth weight baby. Most participants have completed higher education. Massachusetts and Michigan are the most represented out of the states in the population.
Descriptive Table - vlbw, lbw, nbw, mac
#specifying continuous and categorical variables # List of continuous variables <- c ("MAT_AGE_PU" , "MOM_BMI" )# List of categorical variables <- c ("PRE_VIT_RAW" , "VPP_VIT_RAW" , "PRE_LB_NAPHSIS" , "MAT_ED" , "STATE" , "INCOME8" , "PNC_VST" , "MAT_RACE_PU" )
## cleaning data <- data_clean %>% filter (complete.cases (select (., c (cont_vars1, cat_vars1))))
Warning: There were 2 warnings in `filter()`.
The first warning was:
ℹ In argument: `complete.cases(select(., c(cont_vars1, cat_vars1)))`.
Caused by warning:
! Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(cont_vars1)
# Now:
data %>% select(all_of(cont_vars1))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Variables to include in the descriptive table - lbw, vlbw, nbw, mac # Create stratified descriptive table <- CreateTableOne (vars = c (cont_vars1, cat_vars1), strata = "GRAM_NAPHSIS" , data = data_clean)print (table2, showAllLevels = TRUE )
Stratified by GRAM_NAPHSIS
level 0
n 8491
MAT_AGE_PU (mean (SD)) 29.46 (6.13)
MOM_BMI (mean (SD)) 28.67 (7.63)
PRE_VIT_RAW (%) 0 3785 (62.1)
1 2313 (37.9)
VPP_VIT_RAW (%) 0 3349 (45.9)
1 3955 (54.1)
PRE_LB_NAPHSIS (%) 0 4079 (48.3)
1 2146 (25.4)
2 2222 (26.3)
MAT_ED (%) <= 12th grade, no diploma 1014 (12.1)
Bachelor's, Master's, or higher 2444 (29.2)
High school graduate / GED 2371 (28.3)
Some college, no degree 2538 (30.3)
STATE (%) AK 237 ( 2.8)
AL 61 ( 0.7)
AR 283 ( 3.3)
AZ 14 ( 0.2)
CO 155 ( 1.8)
DC 156 ( 1.8)
DE 261 ( 3.1)
GA 254 ( 3.0)
HI 186 ( 2.2)
IA 61 ( 0.7)
IL 340 ( 4.0)
IN 14 ( 0.2)
KS 487 ( 5.7)
KY 63 ( 0.7)
LA 103 ( 1.2)
MA 117 ( 1.4)
MD 329 ( 3.9)
ME 292 ( 3.4)
MI 366 ( 4.3)
MN 40 ( 0.5)
MO 371 ( 4.4)
MP 2 ( 0.0)
MS 362 ( 4.3)
MT 37 ( 0.4)
ND 53 ( 0.6)
NE 89 ( 1.0)
NH 151 ( 1.8)
NJ 81 ( 1.0)
NM 96 ( 1.1)
NY 373 ( 4.4)
OR 81 ( 1.0)
PA 425 ( 5.0)
PR 317 ( 3.7)
RI 284 ( 3.3)
SD 58 ( 0.7)
TN 133 ( 1.6)
UT 457 ( 5.4)
VA 131 ( 1.5)
VT 219 ( 2.6)
WA 69 ( 0.8)
WI 117 ( 1.4)
WV 204 ( 2.4)
WY 118 ( 1.4)
YC 444 ( 5.2)
INCOME8 (%) High income ($85,001 or more) 1520 (20.3)
Low income (0 - $40,000) 4213 (56.2)
Middle ($40,001 - $85,000) 1758 (23.5)
PNC_VST (%) 0 406 ( 5.1)
1 146 ( 1.8)
2 290 ( 3.7)
3 460 ( 5.8)
4 743 ( 9.4)
5 968 (12.2)
6 1024 (12.9)
7 734 ( 9.2)
8 766 ( 9.7)
9 446 ( 5.6)
10 666 ( 8.4)
11 199 ( 2.5)
12 326 ( 4.1)
13 124 ( 1.6)
14 136 ( 1.7)
15 166 ( 2.1)
16 71 ( 0.9)
17 51 ( 0.6)
18 37 ( 0.5)
19 20 ( 0.3)
20 54 ( 0.7)
21 16 ( 0.2)
22 10 ( 0.1)
23 7 ( 0.1)
24 11 ( 0.1)
25 15 ( 0.2)
26 5 ( 0.1)
27 2 ( 0.0)
28 4 ( 0.1)
29 3 ( 0.0)
30 10 ( 0.1)
31 5 ( 0.1)
32 2 ( 0.0)
33 1 ( 0.0)
34 1 ( 0.0)
35 0 ( 0.0)
36 2 ( 0.0)
37 0 ( 0.0)
38 0 ( 0.0)
39 0 ( 0.0)
40 4 ( 0.1)
41 0 ( 0.0)
42 0 ( 0.0)
43 1 ( 0.0)
44 1 ( 0.0)
45 0 ( 0.0)
46 0 ( 0.0)
47 1 ( 0.0)
48 0 ( 0.0)
49 1 ( 0.0)
50 0 ( 0.0)
51 0 ( 0.0)
53 0 ( 0.0)
54 0 ( 0.0)
56 0 ( 0.0)
58 0 ( 0.0)
60 0 ( 0.0)
64 0 ( 0.0)
66 0 ( 0.0)
88 1 ( 0.0)
90 0 ( 0.0)
94 0 ( 0.0)
MAT_RACE_PU (%) American Indian 127 ( 1.6)
Asian 420 ( 5.3)
Black 2339 (29.4)
Hawaiian/Other Pacific Islander 30 ( 0.4)
Other/Multiple Race 746 ( 9.4)
White 4297 (54.0)
Stratified by GRAM_NAPHSIS
1 2 3 p test
n 44367 180131 16702
MAT_AGE_PU (mean (SD)) 29.27 (6.06) 29.23 (5.70) 30.09 (5.35) <0.001
MOM_BMI (mean (SD)) 26.83 (7.15) 26.95 (6.73) 28.79 (7.17) <0.001
PRE_VIT_RAW (%) 19583 (63.0) 79215 (64.1) 7745 (64.3) <0.001
11522 (37.0) 44461 (35.9) 4298 (35.7)
VPP_VIT_RAW (%) 17497 (46.0) 68999 (43.9) 6659 (44.5) <0.001
20560 (54.0) 88265 (56.1) 8295 (55.5)
PRE_LB_NAPHSIS (%) 19350 (43.7) 69783 (38.8) 5019 (30.1) <0.001
12263 (27.7) 57679 (32.1) 5908 (35.4)
12651 (28.6) 52341 (29.1) 5745 (34.5)
MAT_ED (%) 5680 (12.9) 21092 (11.8) 1569 ( 9.5) <0.001
13852 (31.5) 65588 (36.7) 6866 (41.4)
11768 (26.8) 42337 (23.7) 3472 (21.0)
12641 (28.8) 49522 (27.7) 4659 (28.1)
STATE (%) 1184 ( 2.7) 3301 ( 1.8) 603 ( 3.6) <0.001
367 ( 0.8) 3885 ( 2.2) 288 ( 1.7)
1427 ( 3.2) 2652 ( 1.5) 179 ( 1.1)
83 ( 0.2) 1426 ( 0.8) 117 ( 0.7)
1048 ( 2.4) 6919 ( 3.8) 383 ( 2.3)
768 ( 1.7) 1522 ( 0.8) 108 ( 0.6)
1340 ( 3.0) 4152 ( 2.3) 412 ( 2.5)
766 ( 1.7) 2790 ( 1.5) 181 ( 1.1)
1013 ( 2.3) 4124 ( 2.3) 296 ( 1.8)
270 ( 0.6) 3707 ( 2.1) 398 ( 2.4)
1688 ( 3.8) 4998 ( 2.8) 443 ( 2.7)
65 ( 0.1) 733 ( 0.4) 53 ( 0.3)
2314 ( 5.2) 3202 ( 1.8) 328 ( 2.0)
290 ( 0.7) 3313 ( 1.8) 274 ( 1.6)
467 ( 1.1) 4215 ( 2.3) 296 ( 1.8)
611 ( 1.4) 8611 ( 4.8) 747 ( 4.5)
1502 ( 3.4) 3049 ( 1.7) 337 ( 2.0)
1780 ( 4.0) 3208 ( 1.8) 425 ( 2.5)
1795 ( 4.0) 8215 ( 4.6) 699 ( 4.2)
197 ( 0.4) 2952 ( 1.6) 407 ( 2.4)
1793 ( 4.0) 4723 ( 2.6) 454 ( 2.7)
20 ( 0.0) 187 ( 0.1) 7 ( 0.0)
1731 ( 3.9) 2153 ( 1.2) 124 ( 0.7)
302 ( 0.7) 4529 ( 2.5) 462 ( 2.8)
228 ( 0.5) 3383 ( 1.9) 441 ( 2.6)
527 ( 1.2) 6958 ( 3.9) 596 ( 3.6)
970 ( 2.2) 1665 ( 0.9) 250 ( 1.5)
528 ( 1.2) 6724 ( 3.7) 549 ( 3.3)
519 ( 1.2) 6749 ( 3.7) 451 ( 2.7)
1908 ( 4.3) 2447 ( 1.4) 307 ( 1.8)
419 ( 0.9) 6476 ( 3.6) 651 ( 3.9)
2072 ( 4.7) 4744 ( 2.6) 500 ( 3.0)
2261 ( 5.1) 3108 ( 1.7) 137 ( 0.8)
1394 ( 3.1) 3104 ( 1.7) 335 ( 2.0)
332 ( 0.7) 5434 ( 3.0) 593 ( 3.6)
780 ( 1.8) 941 ( 0.5) 75 ( 0.4)
2435 ( 5.5) 6468 ( 3.6) 464 ( 2.8)
581 ( 1.3) 5472 ( 3.0) 575 ( 3.4)
1086 ( 2.4) 4175 ( 2.3) 554 ( 3.3)
449 ( 1.0) 6973 ( 3.9) 843 ( 5.0)
600 ( 1.4) 6518 ( 3.6) 572 ( 3.4)
1223 ( 2.8) 1776 ( 1.0) 186 ( 1.1)
1031 ( 2.3) 2207 ( 1.2) 155 ( 0.9)
2203 ( 5.0) 6243 ( 3.5) 447 ( 2.7)
INCOME8 (%) 9113 (23.3) 45400 (28.4) 4872 (32.0) <0.001
21536 (55.1) 75938 (47.5) 6159 (40.4)
8448 (21.6) 38579 (24.1) 4217 (27.7)
PNC_VST (%) 1055 ( 2.5) 1903 ( 1.1) 124 ( 0.8) <0.001
356 ( 0.8) 697 ( 0.4) 52 ( 0.3)
542 ( 1.3) 1133 ( 0.6) 70 ( 0.4)
717 ( 1.7) 1555 ( 0.9) 111 ( 0.7)
1004 ( 2.4) 2217 ( 1.3) 136 ( 0.8)
1563 ( 3.7) 3227 ( 1.8) 224 ( 1.4)
2319 ( 5.4) 4435 ( 2.5) 344 ( 2.1)
2896 ( 6.8) 6174 ( 3.5) 407 ( 2.5)
4064 ( 9.5) 9771 ( 5.6) 707 ( 4.3)
3935 ( 9.2) 12664 ( 7.2) 1002 ( 6.1)
6080 (14.3) 23817 (13.6) 1891 (11.6)
3675 ( 8.6) 21282 (12.1) 1868 (11.4)
4547 (10.7) 28684 (16.3) 2753 (16.8)
2400 ( 5.6) 18975 (10.8) 2088 (12.8)
1950 ( 4.6) 14152 ( 8.1) 1646 (10.1)
1691 ( 4.0) 9522 ( 5.4) 1159 ( 7.1)
921 ( 2.2) 4997 ( 2.8) 582 ( 3.6)
573 ( 1.3) 2849 ( 1.6) 322 ( 2.0)
486 ( 1.1) 2089 ( 1.2) 263 ( 1.6)
299 ( 0.7) 1230 ( 0.7) 129 ( 0.8)
572 ( 1.3) 1549 ( 0.9) 175 ( 1.1)
176 ( 0.4) 608 ( 0.3) 58 ( 0.4)
130 ( 0.3) 476 ( 0.3) 53 ( 0.3)
102 ( 0.2) 333 ( 0.2) 35 ( 0.2)
82 ( 0.2) 258 ( 0.1) 36 ( 0.2)
111 ( 0.3) 267 ( 0.2) 20 ( 0.1)
46 ( 0.1) 127 ( 0.1) 24 ( 0.1)
40 ( 0.1) 99 ( 0.1) 13 ( 0.1)
34 ( 0.1) 105 ( 0.1) 10 ( 0.1)
23 ( 0.1) 54 ( 0.0) 9 ( 0.1)
89 ( 0.2) 116 ( 0.1) 11 ( 0.1)
7 ( 0.0) 26 ( 0.0) 1 ( 0.0)
16 ( 0.0) 33 ( 0.0) 1 ( 0.0)
7 ( 0.0) 28 ( 0.0) 4 ( 0.0)
8 ( 0.0) 22 ( 0.0) 1 ( 0.0)
13 ( 0.0) 25 ( 0.0) 2 ( 0.0)
8 ( 0.0) 18 ( 0.0) 4 ( 0.0)
8 ( 0.0) 16 ( 0.0) 0 ( 0.0)
5 ( 0.0) 19 ( 0.0) 1 ( 0.0)
8 ( 0.0) 21 ( 0.0) 1 ( 0.0)
28 ( 0.1) 24 ( 0.0) 4 ( 0.0)
4 ( 0.0) 6 ( 0.0) 2 ( 0.0)
4 ( 0.0) 3 ( 0.0) 0 ( 0.0)
2 ( 0.0) 2 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
0 ( 0.0) 3 ( 0.0) 0 ( 0.0)
2 ( 0.0) 1 ( 0.0) 0 ( 0.0)
2 ( 0.0) 2 ( 0.0) 0 ( 0.0)
4 ( 0.0) 1 ( 0.0) 0 ( 0.0)
3 ( 0.0) 3 ( 0.0) 1 ( 0.0)
4 ( 0.0) 6 ( 0.0) 0 ( 0.0)
0 ( 0.0) 0 ( 0.0) 1 ( 0.0)
1 ( 0.0) 0 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
2 ( 0.0) 1 ( 0.0) 0 ( 0.0)
3 ( 0.0) 1 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
0 ( 0.0) 1 ( 0.0) 0 ( 0.0)
MAT_RACE_PU (%) 620 ( 1.5) 6881 ( 4.0) 1061 ( 6.9) <0.001
2687 ( 6.4) 12530 ( 7.3) 567 ( 3.7)
9001 (21.6) 30470 (17.8) 1722 (11.2)
162 ( 0.4) 1027 ( 0.6) 107 ( 0.7)
3880 ( 9.3) 19564 (11.4) 1696 (11.0)
25389 (60.8) 100555 (58.8) 10260 (66.6)
There is a significant difference in maternal age, BMI, pre-pregnancy folic intake, pregnancy folic intake, parity, maternal education, state, income, prenatal care visit count, and race between the different categories of birth weight (very low birth weight, low birth weight, normal birth weight, and macrasomia).
Descriptive Table - yes lbw, no lbw
# Variables to include in the descriptive table - lbw, not lbw # Create stratified descriptive table <- CreateTableOne (vars = c (cont_vars1, cat_vars1), strata = "LBW" , data = data_clean)print (table3, showAllLevels = TRUE )
Stratified by LBW
level 0
n 196833
MAT_AGE_PU (mean (SD)) 29.30 (5.68)
MOM_BMI (mean (SD)) 27.11 (6.78)
PRE_VIT_RAW (%) 0 86960 (64.1)
1 48759 (35.9)
VPP_VIT_RAW (%) 0 75658 (43.9)
1 96560 (56.1)
PRE_LB_NAPHSIS (%) 0 74802 (38.1)
1 63587 (32.4)
2 58086 (29.6)
MAT_ED (%) <= 12th grade, no diploma 22661 (11.6)
Bachelor's, Master's, or higher 72454 (37.1)
High school graduate / GED 45809 (23.5)
Some college, no degree 54181 (27.8)
STATE (%) AK 3904 ( 2.0)
AL 4173 ( 2.1)
AR 2831 ( 1.4)
AZ 1543 ( 0.8)
CO 7302 ( 3.7)
DC 1630 ( 0.8)
DE 4564 ( 2.3)
GA 2971 ( 1.5)
HI 4420 ( 2.2)
IA 4105 ( 2.1)
IL 5441 ( 2.8)
IN 786 ( 0.4)
KS 3530 ( 1.8)
KY 3587 ( 1.8)
LA 4511 ( 2.3)
MA 9358 ( 4.8)
MD 3386 ( 1.7)
ME 3633 ( 1.8)
MI 8914 ( 4.5)
MN 3359 ( 1.7)
MO 5177 ( 2.6)
MP 194 ( 0.1)
MS 2277 ( 1.2)
MT 4991 ( 2.5)
ND 3824 ( 1.9)
NE 7554 ( 3.8)
NH 1915 ( 1.0)
NJ 7273 ( 3.7)
NM 7200 ( 3.7)
NY 2754 ( 1.4)
OR 7127 ( 3.6)
PA 5244 ( 2.7)
PR 3245 ( 1.6)
RI 3439 ( 1.7)
SD 6027 ( 3.1)
TN 1016 ( 0.5)
UT 6932 ( 3.5)
VA 6047 ( 3.1)
VT 4729 ( 2.4)
WA 7816 ( 4.0)
WI 7090 ( 3.6)
WV 1962 ( 1.0)
WY 2362 ( 1.2)
YC 6690 ( 3.4)
INCOME8 (%) High income ($85,001 or more) 50272 (28.7)
Low income (0 - $40,000) 82097 (46.9)
Middle ($40,001 - $85,000) 42796 (24.4)
PNC_VST (%) 0 2027 ( 1.1)
1 749 ( 0.4)
2 1203 ( 0.6)
3 1666 ( 0.9)
4 2353 ( 1.2)
5 3451 ( 1.8)
6 4779 ( 2.5)
7 6581 ( 3.4)
8 10478 ( 5.5)
9 13666 ( 7.1)
10 25708 (13.4)
11 23150 (12.1)
12 31437 (16.4)
13 21063 (11.0)
14 15798 ( 8.2)
15 10681 ( 5.6)
16 5579 ( 2.9)
17 3171 ( 1.7)
18 2352 ( 1.2)
19 1359 ( 0.7)
20 1724 ( 0.9)
21 666 ( 0.3)
22 529 ( 0.3)
23 368 ( 0.2)
24 294 ( 0.2)
25 287 ( 0.1)
26 151 ( 0.1)
27 112 ( 0.1)
28 115 ( 0.1)
29 63 ( 0.0)
30 127 ( 0.1)
31 27 ( 0.0)
32 34 ( 0.0)
33 32 ( 0.0)
34 23 ( 0.0)
35 27 ( 0.0)
36 22 ( 0.0)
37 16 ( 0.0)
38 20 ( 0.0)
39 22 ( 0.0)
40 28 ( 0.0)
41 8 ( 0.0)
42 3 ( 0.0)
43 2 ( 0.0)
44 1 ( 0.0)
45 3 ( 0.0)
46 1 ( 0.0)
47 2 ( 0.0)
48 1 ( 0.0)
49 4 ( 0.0)
50 6 ( 0.0)
51 1 ( 0.0)
53 0 ( 0.0)
54 1 ( 0.0)
56 1 ( 0.0)
58 1 ( 0.0)
60 1 ( 0.0)
64 1 ( 0.0)
66 1 ( 0.0)
88 0 ( 0.0)
90 1 ( 0.0)
94 1 ( 0.0)
MAT_RACE_PU (%) American Indian 7942 ( 4.3)
Asian 13097 ( 7.0)
Black 32192 (17.3)
Hawaiian/Other Pacific Islander 1134 ( 0.6)
Other/Multiple Race 21260 (11.4)
White 110815 (59.4)
Stratified by LBW
1 p test
n 52858
MAT_AGE_PU (mean (SD)) 29.30 (6.07) 0.962
MOM_BMI (mean (SD)) 27.12 (7.26) 0.716
PRE_VIT_RAW (%) 23368 (62.8) <0.001
13835 (37.2)
VPP_VIT_RAW (%) 20846 (46.0) <0.001
24515 (54.0)
PRE_LB_NAPHSIS (%) 23429 (44.4) <0.001
14409 (27.3)
14873 (28.2)
MAT_ED (%) 6694 (12.8) <0.001
16296 (31.2)
14139 (27.0)
15179 (29.0)
STATE (%) 1421 ( 2.7) <0.001
428 ( 0.8)
1710 ( 3.2)
97 ( 0.2)
1203 ( 2.3)
924 ( 1.7)
1601 ( 3.0)
1020 ( 1.9)
1199 ( 2.3)
331 ( 0.6)
2028 ( 3.8)
79 ( 0.1)
2801 ( 5.3)
353 ( 0.7)
570 ( 1.1)
728 ( 1.4)
1831 ( 3.5)
2072 ( 3.9)
2161 ( 4.1)
237 ( 0.4)
2164 ( 4.1)
22 ( 0.0)
2093 ( 4.0)
339 ( 0.6)
281 ( 0.5)
616 ( 1.2)
1121 ( 2.1)
609 ( 1.2)
615 ( 1.2)
2281 ( 4.3)
500 ( 0.9)
2497 ( 4.7)
2578 ( 4.9)
1678 ( 3.2)
390 ( 0.7)
913 ( 1.7)
2892 ( 5.5)
712 ( 1.3)
1305 ( 2.5)
518 ( 1.0)
717 ( 1.4)
1427 ( 2.7)
1149 ( 2.2)
2647 ( 5.0)
INCOME8 (%) 10633 (22.8) <0.001
25749 (55.3)
10206 (21.9)
PNC_VST (%) 1461 ( 2.9) <0.001
502 ( 1.0)
832 ( 1.6)
1177 ( 2.3)
1747 ( 3.5)
2531 ( 5.0)
3343 ( 6.6)
3630 ( 7.2)
4830 ( 9.6)
4381 ( 8.7)
6746 (13.3)
3874 ( 7.7)
4873 ( 9.6)
2524 ( 5.0)
2086 ( 4.1)
1857 ( 3.7)
992 ( 2.0)
624 ( 1.2)
523 ( 1.0)
319 ( 0.6)
626 ( 1.2)
192 ( 0.4)
140 ( 0.3)
109 ( 0.2)
93 ( 0.2)
126 ( 0.2)
51 ( 0.1)
42 ( 0.1)
38 ( 0.1)
26 ( 0.1)
99 ( 0.2)
12 ( 0.0)
18 ( 0.0)
8 ( 0.0)
9 ( 0.0)
13 ( 0.0)
10 ( 0.0)
8 ( 0.0)
5 ( 0.0)
8 ( 0.0)
32 ( 0.1)
4 ( 0.0)
4 ( 0.0)
3 ( 0.0)
1 ( 0.0)
0 ( 0.0)
2 ( 0.0)
3 ( 0.0)
4 ( 0.0)
4 ( 0.0)
4 ( 0.0)
0 ( 0.0)
1 ( 0.0)
0 ( 0.0)
0 ( 0.0)
2 ( 0.0)
3 ( 0.0)
0 ( 0.0)
0 ( 0.0)
1 ( 0.0)
0 ( 0.0)
0 ( 0.0)
MAT_RACE_PU (%) 747 ( 1.5) <0.001
3107 ( 6.3)
11340 (22.8)
192 ( 0.4)
4626 ( 9.3)
29686 (59.7)
There is a significant difference in pre-pregnancy folic intake, pregnancy folic intake, parity, maternal education, state, income, prenatal care visit count, and race between low birth weight.
2x2 Tables
# 2x2 tables addmargins (table (data_clean$ PRE_VIT_RAW, data_clean$ LBW,dnn = c ("Pre-pregnancy Folic Intake" ,"LBW" )))
LBW
Pre-pregnancy Folic Intake 0 1 Sum
0 86960 23368 110328
1 48759 13835 62594
Sum 135719 37203 172922
addmargins (table (data_clean$ VPP_VIT_RAW, data_clean$ LBW,dnn = c ("Pre-pregnancy Folic Intake" ,"LBW" )))
LBW
Pre-pregnancy Folic Intake 0 1 Sum
0 75658 20846 96504
1 96560 24515 121075
Sum 172218 45361 217579
For Pre-pregnancy- 86960 individuals who did not have low birth weight and did not have pre-pregnancy folic intake. 23368 individuals who had low birth weight and did not have pre-pregnancy folic intake. 48759 individuals who did not have low birth weight and had pre-pregnancy folic intake. 13835 individuals who had low birth weight and had pre-pregnancy folic intake.
For pregnancy- 75658 individuals who did not have low birth weight and did not have folic intake during pregnancy. 20846 individuals who had low birth weight and did not have folic intake during pregnancy. 96560 individuals who did not have low birth weight and have folic intake during pregnancy. 24515 individuals who had low birth weight and have folic intake during pregnancy.
# For PRE_VIT_RAW vs. LBW chisq.test (table (data_clean$ PRE_VIT_RAW, data_clean$ LBW))
Pearson's Chi-squared test with Yates' continuity correction
data: table(data_clean$PRE_VIT_RAW, data_clean$LBW)
X-squared = 20.063, df = 1, p-value = 7.494e-06
# For VPP_VIT_RAW vs. LBW chisq.test (table (data_clean$ VPP_VIT_RAW, data_clean$ LBW))
Pearson's Chi-squared test with Yates' continuity correction
data: table(data_clean$VPP_VIT_RAW, data_clean$LBW)
X-squared = 59.526, df = 1, p-value = 1.207e-14
Both pre-pregnancy folic intake and folic intake supplementation during pregnancy appear to be significantly associated with low birth weight. These findings suggest that maternal folic intake, either before or during pregnancy, could play an important role in birth weight outcomes.
Bar Plots
Percentage by LBW
%>% filter (! is.na (LBW)) %>% group_by (LBW) %>% summarise (count = n ()) %>% mutate (percentage = count / sum (count) * 100 ) %>% ggplot (aes (x = as.factor (LBW), y = percentage, fill = as.factor (LBW))) + geom_bar (stat = "identity" , color = "black" ) + # Use stat = "identity" for percentages scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), # Assign colors to LBW categories labels = c ("0" = "No" , "1" = "Yes" ) # Adjust legend labels + scale_x_discrete (labels = c ("0" = "No" , "1" = "Yes" )) + # Adjust x-axis labels labs (title = "Percentage of Low Birth Weight (LBW) Cases" ,x = "Low Birth Weight" ,y = "Percentage" ,fill = "LBW Status" # Adjust legend title + scale_y_continuous (labels = scales:: percent_format (scale = 1 )) + # Format y-axis as percentage theme_minimal () # For cleaner plot appearance
In looking at LBW in this population we saw that 78.8% did not have a LBW baby, while 21.2% did.
###Percentage by Pre-pregnancy & During Pregnancy Folic Intake
%>% filter (! is.na (PRE_VIT_RAW), ! is.na (VPP_VIT_RAW)) %>% pivot_longer (cols = c (PRE_VIT_RAW, VPP_VIT_RAW), names_to = "Folic_Acid_Stage" , values_to = "Folic_Acid_Intake" %>% group_by (Folic_Acid_Stage, Folic_Acid_Intake) %>% summarise (count = n (), .groups = "drop" ) %>% group_by (Folic_Acid_Stage) %>% # Group by Folic_Acid_Stage for percentage calculation mutate (percentage = count / sum (count) * 100 ) %>% # Compute percentage within each stage ggplot (aes (x = as.factor (Folic_Acid_Intake), y = percentage, fill = as.factor (Folic_Acid_Intake))) + geom_bar (stat = "identity" , position = "dodge" , color = "black" ) + # Dodge position for side-by-side bars scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), # Assign colors for No and Yes labels = c ("0" = "No" , "1" = "Yes" ) # Adjust legend labels + scale_x_discrete (labels = c ("0" = "No" , "1" = "Yes" )) + # Adjust x-axis labels facet_wrap (~ Folic_Acid_Stage, labeller = labeller (Folic_Acid_Stage = c ("PRE_VIT_RAW" = "Pre-Pregnancy Intake" , "VPP_VIT_RAW" = "Pregnancy Intake" )) # Custom facet labels + labs (title = "Percentage of Folic Acid Intake Pre and During Pregnancy" ,x = "Folic Acid Intake" ,y = "Percentage" ,fill = "Folic Acid Intake" # Legend title + scale_y_continuous (labels = scales:: percent_format (scale = 1 )) + # Format y-axis as percentage theme_minimal () # For cleaner plot appearance
In looking at pre-pregnancy folic acid intake, we saw that 36.2% of women did take folic acid, while 63.8% did not.
During pregnancy folic acid intake increased with 55.6% of women taking folic acid while 44.4% did not.
Count of People with Folic Acid Intake (Pre and During Pregnancy Bar Plots)
# Combine both Pre and During Pregnancy Folic Acid Intake Counts in One Plot %>% gather (key = "Pregnancy_Status" , value = "Folic_Acid_Intake" , PRE_VIT_RAW, VPP_VIT_RAW) %>% mutate (Folic_Acid_Intake = factor (Folic_Acid_Intake, levels = c (0 , 1 ), labels = c ("No" , "Yes" ))) %>% filter (! is.na (Folic_Acid_Intake)) %>% ggplot (aes (x = Folic_Acid_Intake, fill = Folic_Acid_Intake)) + geom_bar () + scale_fill_manual (values = c ("blue" , "red" )) + facet_wrap (~ Pregnancy_Status, scales = "free" , labeller = labeller (Pregnancy_Status = c ("PRE_VIT_RAW" = "Pre-Pregnancy" , "VPP_VIT_RAW" = "During Pregnancy" ))) + labs (x = "Folic Acid Intake" , y = "Count of People" , fill = "Folic Acid Intake" ,title = "Count of People with Folic Acid Intake (Pre and During Pregnancy)" ) + theme_minimal () + theme (legend.position = "top" )
# Count people by folic acid intake status for both pre-pregnancy and during pregnancy %>% gather (key = "Pregnancy_Status" , value = "Folic_Acid_Intake" , PRE_VIT_RAW, VPP_VIT_RAW) %>% mutate (Folic_Acid_Intake = factor (Folic_Acid_Intake, levels = c (0 , 1 ), labels = c ("No" , "Yes" ))) %>% filter (! is.na (Folic_Acid_Intake)) %>% count (Pregnancy_Status, Folic_Acid_Intake)
# A tibble: 4 × 3
Pregnancy_Status Folic_Acid_Intake n
<chr> <fct> <int>
1 PRE_VIT_RAW No 110466
2 PRE_VIT_RAW Yes 62669
3 VPP_VIT_RAW No 96628
4 VPP_VIT_RAW Yes 121177
A larger proportion of people took folic acid during pregnancy (121,177 people) compared to before pregnancy (62,669 people). Similarly, more people did not take folic acid before pregnancy (110,466 people) than during pregnancy (96,628 people).
Pre-Pregnancy Folic Acid Intake Bar Plots
## barplot of Pre-Pregnancy Folic Acid Intake and LBW Status ggplot (data_clean %>% filter (! is.na (PRE_VIT_RAW), ! is.na (LBW)), aes (x = PRE_VIT_RAW, fill = factor (LBW))) + geom_bar (position = "fill" ) + # "fill" makes the bars proportional (percentage) scale_y_continuous (labels = scales:: percent) + scale_x_discrete (labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes on x-axis scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes in the legend labs (x = "Pre-Pregnancy Folic Acid Intake" , y = "Percentage of Individuals" , fill = "LBW Status" ,title = "Pre-Pregnancy Folic Acid Intake and LBW Status" ) + theme_minimal ()
%>% filter (! is.na (PRE_VIT_RAW), ! is.na (LBW)) %>% count (PRE_VIT_RAW, LBW)
# A tibble: 4 × 3
PRE_VIT_RAW LBW n
<fct> <fct> <int>
1 0 0 86960
2 0 1 23368
3 1 0 48759
4 1 1 13835
Pre-pregnancy: For people with no folic acid intake: 78.8% do not have LBW 21.2% have LBW
For people with folic acid intake: 77.9% do not have LBW 22.1% have LBW
During Pregnancy Folic Acid Intake Bar Plots
## bar plot of During Pregnancy Folic Acid Intake Bar Plots ggplot (data_clean %>% filter (! is.na (VPP_VIT_RAW), ! is.na (LBW)), aes (x = VPP_VIT_RAW, fill = factor (LBW))) + geom_bar (position = "fill" ) + # "fill" makes the bars proportional (percentage) scale_y_continuous (labels = scales:: percent) + scale_x_discrete (labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes on x-axis scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes in the legend labs (x = "During Pregnancy Folic Acid Intake" , y = "Percentage of Individuals" , fill = "LBW Status" ,title = "During Pregnancy Folic Acid Intake and LBW Status" ) + theme_minimal ()
%>% filter (! is.na (VPP_VIT_RAW), ! is.na (LBW)) %>% count (VPP_VIT_RAW, LBW)
# A tibble: 4 × 3
VPP_VIT_RAW LBW n
<fct> <fct> <int>
1 0 0 75658
2 0 1 20846
3 1 0 96560
4 1 1 24515
During pregnancy: For people with no folic acid intake during pregnancy: 78.4% do not have LBW 21.6% have LBW For people with folic acid intake during pregnancy: 79.8% do not have LBW 20.2% have LBW
Combined Pregnancy Folic Acid Intake Bar Plots
<- data_clean %>% filter (! is.na (PRE_VIT_RAW), ! is.na (VPP_VIT_RAW), ! is.na (LBW)) %>% pivot_longer (cols = c (PRE_VIT_RAW, VPP_VIT_RAW), names_to = "Folic_Acid_Stage" , values_to = "Folic_Acid_Intake" ) %>% mutate (Folic_Acid_Stage = recode (Folic_Acid_Stage, "PRE_VIT_RAW" = "Pre-Pregnancy" , "VPP_VIT_RAW" = "During Pregnancy" ),Folic_Acid_Stage = factor (Folic_Acid_Stage, levels = c ("Pre-Pregnancy" , "During Pregnancy" ))) %>% group_by (Folic_Acid_Stage, Folic_Acid_Intake, LBW) %>% summarise (count = n ()) %>% ungroup () %>% group_by (Folic_Acid_Stage, Folic_Acid_Intake) %>% mutate (total = sum (count),percentage = count / total * 100 ) %>% ungroup ()
`summarise()` has grouped output by 'Folic_Acid_Stage', 'Folic_Acid_Intake'.
You can override using the `.groups` argument.
# Plotting ggplot (data_combined, aes (x = factor (Folic_Acid_Intake), y = percentage, fill = factor (LBW))) + geom_bar (stat = "identity" , position = "dodge" , width = 0.7 ) + # Use stat = "identity" to plot percentages scale_y_continuous (labels = scales:: percent_format (scale = 1 )) + # Format y-axis as percentage scale_x_discrete (labels = c ("0" = "No" , "1" = "Yes" )) + # Customize x-axis labels scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Customize colors labs (x = "Folic Acid Intake" , y = "Percentage of Individuals" , fill = "LBW Status" , title = "Folic Acid Intake (Pre and During Pregnancy) and LBW Status" ) + facet_wrap (~ Folic_Acid_Stage) + # Facet by folic acid intake stage theme_minimal ()
Looking at pre-pregnancy folic acid intake and during pregnancy folic acid intake and LBW status, we saw that for those Pre-pregnancy who didnt take folic acid: 78.8% do not have LBW 21.2% have LBW. and for people with folic acid intake: 77.9% do not have LBW 22.1% have LBW.
For those During pregnancy with no folic acid intake: 78.4% do not have LBW 21.6% have LBW and For people with folic acid intake during pregnancy: 79.8% do not have LBW 20.2% have LBW.
Although the differences in LBW rates between those who took folic acid and those who did not are small for both, this might suggest that taking folic acid during pregnancy may offer a slightly greater protective effect against low birth weight than taking it before pregnancy.
##Covariates by Outcomes
Parity
##bar plot of Parity and LBW Status ggplot (data_clean %>% filter (! is.na (PRE_LB_NAPHSIS), ! is.na (LBW)), aes (x = PRE_LB_NAPHSIS, fill = factor (LBW))) + geom_bar (position = "fill" ) + # "fill" makes the bars proportional (percentage) scale_y_continuous (labels = scales:: percent) + scale_x_discrete (labels = c ("0" = "No children" , "1" = "One child" , "2" = "2+ children" )) + # Change 0, 1 to No, Yes on x-axis scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes in the legend labs (x = "Parity" , y = "Percentage of Individuals" , fill = "LBW Status" ,title = "Parity and LBW Status" ) + theme_minimal ()
%>% filter (! is.na (PRE_LB_NAPHSIS), ! is.na (LBW)) %>% count (PRE_LB_NAPHSIS, LBW)
# A tibble: 6 × 3
PRE_LB_NAPHSIS LBW n
<fct> <fct> <int>
1 0 0 74802
2 0 1 23429
3 1 0 63587
4 1 1 14409
5 2 0 58086
6 2 1 14873
For PRE_LB_NAPHSIS = 0: 76.2% do not have LBW 23.8% have LBW
For PRE_LB_NAPHSIS = 1: 81.5% do not have LBW 18.5% have LBW
For PRE_LB_NAPHSIS = 2: 79.6% do not have LBW 20.4% have LBW
First-time pregnancies (PRE_LB_NAPHSIS = 0) have the highest LBW risk (23.8%). One prior pregnancy (PRE_LB_NAPHSIS = 1) seems to have the best outcomes with the lowest proportion of LBW (18.5%). These patterns suggest that maternal history of prior pregnancies influences birth weight outcomes, potentially due to biological or healthcare-related factors that improve with experience or repeated exposure to pregnancy.
Maternal Age
## box plot of Maternal Age by LBW Status %>% filter (! is.na (MAT_AGE_PU), ! is.na (LBW)) %>% ggplot (aes (x = factor (LBW, levels = c ("0" , "1" ), labels = c ("No" , "Yes" )), y = MAT_AGE_PU, color = factor (LBW, levels = c ("0" , "1" ), labels = c ("No" , "Yes" )))) + geom_boxplot (width = 0.5 ) + geom_jitter (width = 0.1 , alpha = 0.3 ) + ggtitle ("Maternal Age by LBW Status" ) + xlab ("LBW Status" ) + ylab ("Maternal Age (Years)" ) + scale_color_manual (name = "LBW Status" , values = c ("red" , "blue" )) + theme_minimal () # Use a built-in theme here
<- data_clean %>% filter (! is.na (MAT_AGE_PU), ! is.na (LBW)) %>% group_by (LBW) %>% summarize (Median_Age = median (MAT_AGE_PU),IQR_Lower = quantile (MAT_AGE_PU, 0.25 ),IQR_Upper = quantile (MAT_AGE_PU, 0.75 )print (ci_data)
# A tibble: 2 × 4
LBW Median_Age IQR_Lower IQR_Upper
<fct> <dbl> <dbl> <dbl>
1 0 29 25 33
2 1 29 25 34
For individuals without LBW (LBW group 0), the central value of maternal age is 29 years, with the middle 50% of the ages falling between 25 and 33 years. For individuals with LBW, the median maternal age is also 29 years. However, the range of ages for the middle 50% of individuals is slightly broader, spanning from 25 to 34 years.
Maternal BMI
## box plot of Maternal BMI by LBW Status %>% filter (! is.na (MOM_BMI), ! is.na (LBW)) %>% ggplot (aes (x = factor (LBW, levels = c ("0" , "1" ), labels = c ("No" , "Yes" )), y = MOM_BMI, color = factor (LBW, levels = c ("0" , "1" ), labels = c ("No" , "Yes" )))) + geom_boxplot (width = 0.5 ) + geom_jitter (width = 0.1 , alpha = 0.3 ) + ggtitle ("Maternal BMI by LBW Status" ) + xlab ("LBW Status" ) + ylab ("Maternal BMI (kg/m2)" ) + scale_color_manual (name = "LBW Status" , values = c ("red" , "blue" )) + theme_minimal () # Use a built-in theme here
<- data_clean %>% filter (! is.na (MOM_BMI), ! is.na (LBW)) %>% group_by (LBW) %>% summarize (Median_BMI = median (MOM_BMI),IQR_Lower = quantile (MOM_BMI, 0.25 ),IQR_Upper = quantile (MOM_BMI, 0.75 )print (ci_data)
# A tibble: 2 × 4
LBW Median_BMI IQR_Lower IQR_Upper
<fct> <dbl> <dbl> <dbl>
1 0 25.6 22.2 30.5
2 1 25.5 21.8 30.9
For mothers whose babies did not have low birth weight, the middle 50% of BMI range from 22.15 to 30.54 kg/m^2;, with a median of 25.61 kg/m^2. For mothers whose babies did have low birth weight, the middle 50% of BMI range from 21.79 to 30.91 kg/m^2, with a median of 25.51 kg/m^2.
Maternal Education
## bar plot of Maternal Education and LBW Status ggplot (data_clean %>% filter (! is.na (MAT_ED), ! is.na (LBW)) %>% mutate (MAT_ED = factor (MAT_ED, levels = c ("<= 12th grade, no diploma" , "High school graduate / GED" , "Some college, no degree" , "Bachelor's, Master's, or higher" ))),aes (x = MAT_ED, fill = factor (LBW))) + geom_bar (position = "fill" ) + # "fill" makes the bars proportional (percentage) scale_y_continuous (labels = scales:: percent) + scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Custom colors for LBW labs (x = "Maternal Education" , y = "Percentage of Individuals" , fill = "LBW Status" , title = "Maternal Education and LBW Status" + theme_minimal () + theme (axis.text.x = element_text (angle = 45 , hjust = 1 ))
%>% filter (! is.na (MAT_ED), ! is.na (LBW)) %>% count (MAT_ED, LBW)
# A tibble: 8 × 3
MAT_ED LBW n
<fct> <fct> <int>
1 <= 12th grade, no diploma 0 22661
2 <= 12th grade, no diploma 1 6694
3 Bachelor's, Master's, or higher 0 72454
4 Bachelor's, Master's, or higher 1 16296
5 High school graduate / GED 0 45809
6 High school graduate / GED 1 14139
7 Some college, no degree 0 54181
8 Some college, no degree 1 15179
<= 12th grade, no diploma: No LBW: 77.2% of individuals without a diploma had babies with normal birth weight. LBW: 22.8% of individuals without a diploma had babies with low birth weight.
High school graduate / GED: No LBW: 76.4% of high school graduates had babies with normal birth weight. LBW: 23.6% of high school graduates had babies with low birth weight.
Some college, no degree: No LBW: 78.1% of individuals with some college education had babies with normal birth weight. LBW: 21.9% of individuals with some college education had babies with low birth weight.
Bachelor’s, Master’s, or higher: No LBW: 81.6% of individuals with higher education had babies with normal birth weight. LBW: 18.4% of individuals with higher education had babies with low birth weight.
Higher maternal education is associated with a lower proportion of low birth weight outcomes.
Income
ggplot (data_clean %>% filter (! is.na (INCOME8), ! is.na (LBW)) %>% mutate (INCOME8 = factor (INCOME8, levels = c ("Low income (0 - $40,000)" , "Middle ($40,001 - $85,000)" , "High income ($85,001 or more)" ))),aes (x = INCOME8, fill = factor (LBW))) + geom_bar (position = "fill" ) + # "fill" makes the bars proportional (percentage) scale_y_continuous (labels = scales:: percent) + scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes in the legend labs (x = "Income" , y = "Percentage of Individuals" , fill = "LBW Status" ,title = "Income and LBW Status" ) + theme_minimal () + theme (axis.text.x = element_text (angle = 45 , hjust = 1 ))
%>% filter (! is.na (INCOME8), ! is.na (LBW)) %>% count (INCOME8, LBW)
# A tibble: 6 × 3
INCOME8 LBW n
<fct> <fct> <int>
1 High income ($85,001 or more) 0 50272
2 High income ($85,001 or more) 1 10633
3 Low income (0 - $40,000) 0 82097
4 Low income (0 - $40,000) 1 25749
5 Middle ($40,001 - $85,000) 0 42796
6 Middle ($40,001 - $85,000) 1 10206
High Income ($85,001 or more) 82.6% do not have LBW. 17.4% have LBW.
Low Income ($0 - $40,000) 76.1% do not have LBW. 23.9% have LBW.
Middle Income ($40,001 - $85,000) 80.8% do not have LBW. 19.2% have LBW.
Income level is inversely associated with LBW risk: higher income correlates with lower LBW percentages. The low-income group shows the highest proportion of LBW (23.9%), emphasizing the role of socioeconomic disparities in adverse birth outcomes.
Number of Prenatal Care
## clean data, make numeric $ PNC_VST <- as.numeric (data_clean$ PNC_VST)## bar plot of Number of Prenatal Care %>% filter (! is.na (PNC_VST), ! is.na (LBW)) %>% ggplot (aes (x = factor (LBW, levels = c ("0" , "1" ), labels = c ("No" , "Yes" )), y = PNC_VST, color = factor (LBW, levels = c ("0" , "1" ), labels = c ("No" , "Yes" )))) + geom_boxplot (width = 0.5 ) + geom_jitter (width = 0.1 , alpha = 0.3 ) + ggtitle ("Number of Prenatal Care by LBW Status" ) + xlab ("LBW Status" ) + ylab ("Number of Prenatal Care (Count)" ) + scale_color_manual (name = "LBW Status" , values = c ("red" , "blue" )) + theme_minimal () # Use a built-in theme here
<- data_clean %>% mutate (PNC_VST = as.numeric (as.character (PNC_VST)))<- data_clean %>% filter (! is.na (PNC_VST), ! is.na (LBW)) %>% group_by (LBW) %>% summarize (Median_PNC_VST = median (PNC_VST),IQR_Lower = quantile (PNC_VST, 0.25 ),IQR_Upper = quantile (PNC_VST, 0.75 )print (ci_data)
# A tibble: 2 × 4
LBW Median_PNC_VST IQR_Lower IQR_Upper
<fct> <dbl> <dbl> <dbl>
1 0 13 11 14
2 1 11 8 13
For mothers whose babies did not have low birth weight, the middle 50% of BMI range from 10 to 13, with a median of 12. For mothers whose babies did have low birth weight, the middle 50% of BMI range from 7 to 12, with a median of 10.
Maternal Race
## bar plot of Maternal Race ggplot (data_clean %>% filter (! is.na (MAT_RACE_PU), ! is.na (LBW)), aes (x = MAT_RACE_PU, fill = factor (LBW))) + geom_bar (position = "fill" ) + # "fill" makes the bars proportional (percentage) scale_y_continuous (labels = scales:: percent) + scale_x_discrete (labels = c ("White" = "White" , "Black" = "Black" , "American Indian" = "American Indian" , "Alaska Native" = "Alaska Native" , "Asian" = "Asian" , "Hawaiian/Other Pacific Islander" = "Hawaiian/Other Pacific Islander" , "Other/Multiple Race" = "Other/Multiple Race" )) + # Change 0, 1 to No, Yes on x-axis scale_fill_manual (values = c ("0" = "blue" , "1" = "red" ), labels = c ("0" = "No" , "1" = "Yes" )) + # Change 0, 1 to No, Yes in the legend labs (x = "Maternal Race" , y = "Percentage of Individuals" , fill = "LBW Status" ,title = "Maternal Race and LBW Status" ) + theme (axis.text.x = element_text (angle = 45 , hjust = 1 )) # Rotate x-axis labels for readability
%>% filter (! is.na (MAT_RACE_PU), ! is.na (LBW)) %>% count (MAT_RACE_PU, LBW)
# A tibble: 12 × 3
MAT_RACE_PU LBW n
<fct> <fct> <int>
1 American Indian 0 7942
2 American Indian 1 747
3 Asian 0 13097
4 Asian 1 3107
5 Black 0 32192
6 Black 1 11340
7 Hawaiian/Other Pacific Islander 0 1134
8 Hawaiian/Other Pacific Islander 1 192
9 Other/Multiple Race 0 21260
10 Other/Multiple Race 1 4626
11 White 0 110815
12 White 1 29686
American Indian 91.4% do not have LBW. 8.6% have LBW. Asian 80.8% do not have LBW. 19.2% have LBW. Black 73.9% do not have LBW. 26.1% have LBW. Hawaiian/Other Pacific Islander 85.5% do not have LBW. 14.5% have LBW. Other/Multiple Race 82.1% do not have LBW. 17.9% have LBW. White 78.9% do not have LBW. 21.1% have LBW.
The data suggests that the Black racial group has the highest proportion of LBW cases (26.1%), followed by White (21.1%) and Asian (19.2%) individuals. The American Indian and Hawaiian/Other Pacific Islander groups show the lowest rates of LBW. This disparity highlights the potential need for addressing race-related factors contributing to LBW and suggests the importance of tailored interventions for different racial groups.
Dot plot of Occurrences of LBW for each state
# Summarize the data: Count occurrences of LBW for each state <- data_clean %>% group_by (STATE, LBW) %>% summarise (count = n (), .groups = 'drop' ) %>% pivot_wider (names_from = LBW, values_from = count, values_fill = list (count = 0 )) # Reshape the data # Create the dot plot with enhanced color scale ggplot (heatmap_data, aes (x = STATE, y = factor (` 1 ` ), size = ` 1 ` , color = ` 1 ` )) + # Use the '1' column for LBW=1 geom_point (shape = 16 , alpha = 0.7 ) + # Use dots (size indicates count) scale_size_continuous (range = c (3 , 10 )) + # Adjust size of dots scale_color_viridis_c (option = "inferno" , begin = 0.2 , end = 1 ) + # Use the 'inferno' color palette for better contrast labs (title = "Dot Plot of Low Birth Weight by State" ,x = "State" , y = "Number of Low Birth Weight (Count)" ,color = "Count" , size = "Count" ) + theme_minimal () + theme (axis.text.x = element_text (angle = 45 , hjust = 1 )) + theme (legend.position = "bottom" ) # Position the legend at the bottom
Top 5 Places with Highest LBW Counts: Kansas, Utah, New York City, Puerto Rico, Pennsylvania
Top 5 Places with Highest LBW Counts: Northern Mariana, Indiana, Arizona, Minnesota, North Dakota
Dot plot of Rates of LBW for each state
# Summarize the data: Calculate the LBW rate for each state <- data_clean %>% group_by (STATE, LBW) %>% summarise (count = n (), .groups = 'drop' ) %>% pivot_wider (names_from = LBW, values_from = count, values_fill = list (count = 0 )) %>% # Reshape the data mutate (rate = ` 1 ` / (` 0 ` + ` 1 ` )) # Calculate rate of LBW # Create the dot plot with enhanced color scale using rates ggplot (heatmap_data, aes (x = STATE, y = rate, size = rate, color = rate)) + # Use 'rate' column geom_point (shape = 16 , alpha = 0.7 ) + # Use dots (size indicates rate) scale_size_continuous (range = c (3 , 10 )) + # Adjust size of dots scale_color_viridis_c (option = "inferno" , begin = 0.2 , end = 1 ) + # Use 'inferno' color palette for rates labs (title = "Dot Plot of Low Birth Weight Rate by State" ,x = "State" , y = "Low Birth Weight Rate" ,color = "Rate" , size = "Rate" ) + theme_minimal () + theme (axis.text.x = element_text (angle = 45 , hjust = 1 )) + theme (legend.position = "bottom" ) # Position the legend at the bottom
In looking at LBW by states, we saw the Top 5 states with the highest LBW rates—Mississippi (MS), Tennessee (TN), New York (NY), Puerto Rico (PR), and Kansas (KS)—highlight regions where disparities may be more pronounced.
Logistic Regression
# Set reference categories only for categorical variables <- data_clean %>% mutate (PRE_LB_NAPHSIS = relevel ( PRE_LB_NAPHSIS, ref = "0" ),MAT_ED = relevel (MAT_ED, ref = "Bachelor's, Master's, or higher" ), # Set reference for MAT_ED INCOME8 = relevel (INCOME8, ref = "Low income (0 - $40,000)" ), # Set reference for INCOME8 MAT_RACE_PU = relevel (MAT_RACE_PU, ref = "White" ) # Set reference for MAT_RACE_PU (if you want to control this)
Pre-Preg
# Unadjusted logistic regression for LBW with pre-pregnancy folic acid <- glm (~ PRE_VIT_RAW,data = data_clean,family = binomial ()# Adjusted logistic regression for LBW with pre-pregnancy folic acid <- glm (~ PRE_VIT_RAW + MAT_AGE_PU + MOM_BMI + PRE_LB_NAPHSIS + MAT_ED + STATE + INCOME8 + PNC_VST + MAT_RACE_PU,data = data_clean,family = binomial ()# Display unadjusted and adjusted results for pre-pregnancy folic acid <- summary (logit_prepreg_unadjusted, exponentiate = TRUE , conf.int = TRUE )<- summary (logit_prepreg_adjusted, exponentiate = TRUE , conf.int = TRUE )print (prepreg_unadjusted_results)
Call:
glm(formula = LBW ~ PRE_VIT_RAW, family = binomial(), data = data_clean)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.314081 0.007368 -178.340 < 2e-16 ***
PRE_VIT_RAW1 0.054392 0.012128 4.485 7.29e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 180078 on 172921 degrees of freedom
Residual deviance: 180058 on 172920 degrees of freedom
(77048 observations deleted due to missingness)
AIC: 180062
Number of Fisher Scoring iterations: 4
print (prepreg_adjusted_results)
Call:
glm(formula = LBW ~ PRE_VIT_RAW + MAT_AGE_PU + MOM_BMI + PRE_LB_NAPHSIS +
MAT_ED + STATE + INCOME8 + PNC_VST + MAT_RACE_PU, family = binomial(),
data = data_clean)
Coefficients:
Estimate Std. Error z value
(Intercept) -2.184843 0.094395 -23.146
PRE_VIT_RAW1 0.134830 0.015532 8.681
MAT_AGE_PU 0.036719 0.001585 23.173
MOM_BMI 0.003104 0.001067 2.908
PRE_LB_NAPHSIS1 -0.438291 0.017520 -25.016
PRE_LB_NAPHSIS2 -0.449601 0.019979 -22.503
MAT_ED<= 12th grade, no diploma 0.364458 0.033588 10.851
MAT_EDHigh school graduate / GED 0.333926 0.024186 13.806
MAT_EDSome college, no degree 0.265249 0.020278 13.081
STATEAR 1.782119 0.086810 20.529
STATEAZ -0.454712 0.169925 -2.676
STATECO 0.737502 0.084082 8.771
STATEDC 1.558610 0.094474 16.498
STATEDE 1.319873 0.084204 15.675
STATEGA 1.232678 0.092969 13.259
STATEIA -0.015534 0.109172 -0.142
STATEIL 1.494443 0.082527 18.108
STATEIN 0.331745 0.159502 2.080
STATEKS 2.276395 0.081549 27.914
STATEKY 0.051325 0.105413 0.487
STATELA 0.271753 0.095738 2.838
STATEMA -0.173367 0.089341 -1.941
STATEMD 1.885614 0.084108 22.419
STATEME 2.178068 0.082288 26.469
STATEMI 1.104814 0.081512 13.554
STATEMN -0.113131 0.111134 -1.018
STATEMO 1.578770 0.082545 19.126
STATEMP -0.080495 0.410965 -0.196
STATEMS 2.291607 0.086262 26.566
STATEMT -0.066681 0.103487 -0.644
STATEND 0.032782 0.111734 0.293
STATENE -0.126068 0.096077 -1.312
STATENH 2.202729 0.087008 25.316
STATENJ -0.085199 0.096222 -0.885
STATENM -0.087331 0.095661 -0.913
STATENY 2.261078 0.083079 27.216
STATEOR -0.200307 0.098497 -2.034
STATEPA 1.771498 0.081830 21.649
STATEPR 2.312509 0.083095 27.830
STATERI 1.907780 0.086864 21.963
STATESD -0.212164 0.104777 -2.025
STATETN 2.323113 0.097920 23.725
STATEUT 1.860361 0.080214 23.193
STATEVA 0.341180 0.091857 3.714
STATEWA -0.293864 0.098476 -2.984
STATEWI 0.128089 0.091292 1.403
STATEWV 2.221241 0.090611 24.514
STATEWY 1.900310 0.088635 21.440
STATEYC 1.401712 0.082051 17.083
INCOME8High income ($85,001 or more) -0.326586 0.023569 -13.857
INCOME8Middle ($40,001 - $85,000) -0.213725 0.020450 -10.451
PNC_VST -0.119249 0.002021 -59.002
MAT_RACE_PUAmerican Indian -0.441124 0.064154 -6.876
MAT_RACE_PUAsian 0.378689 0.033651 11.253
MAT_RACE_PUBlack 0.387403 0.021789 17.780
MAT_RACE_PUHawaiian/Other Pacific Islander -0.268160 0.180504 -1.486
MAT_RACE_PUOther/Multiple Race 0.070205 0.029951 2.344
Pr(>|z|)
(Intercept) < 2e-16 ***
PRE_VIT_RAW1 < 2e-16 ***
MAT_AGE_PU < 2e-16 ***
MOM_BMI 0.003635 **
PRE_LB_NAPHSIS1 < 2e-16 ***
PRE_LB_NAPHSIS2 < 2e-16 ***
MAT_ED<= 12th grade, no diploma < 2e-16 ***
MAT_EDHigh school graduate / GED < 2e-16 ***
MAT_EDSome college, no degree < 2e-16 ***
STATEAR < 2e-16 ***
STATEAZ 0.007452 **
STATECO < 2e-16 ***
STATEDC < 2e-16 ***
STATEDE < 2e-16 ***
STATEGA < 2e-16 ***
STATEIA 0.886849
STATEIL < 2e-16 ***
STATEIN 0.037536 *
STATEKS < 2e-16 ***
STATEKY 0.626329
STATELA 0.004533 **
STATEMA 0.052317 .
STATEMD < 2e-16 ***
STATEME < 2e-16 ***
STATEMI < 2e-16 ***
STATEMN 0.308692
STATEMO < 2e-16 ***
STATEMP 0.844714
STATEMS < 2e-16 ***
STATEMT 0.519353
STATEND 0.769224
STATENE 0.189469
STATENH < 2e-16 ***
STATENJ 0.375917
STATENM 0.361287
STATENY < 2e-16 ***
STATEOR 0.041988 *
STATEPA < 2e-16 ***
STATEPR < 2e-16 ***
STATERI < 2e-16 ***
STATESD 0.042877 *
STATETN < 2e-16 ***
STATEUT < 2e-16 ***
STATEVA 0.000204 ***
STATEWA 0.002844 **
STATEWI 0.160597
STATEWV < 2e-16 ***
STATEWY < 2e-16 ***
STATEYC < 2e-16 ***
INCOME8High income ($85,001 or more) < 2e-16 ***
INCOME8Middle ($40,001 - $85,000) < 2e-16 ***
PNC_VST < 2e-16 ***
MAT_RACE_PUAmerican Indian 6.15e-12 ***
MAT_RACE_PUAsian < 2e-16 ***
MAT_RACE_PUBlack < 2e-16 ***
MAT_RACE_PUHawaiian/Other Pacific Islander 0.137380
MAT_RACE_PUOther/Multiple Race 0.019080 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 143239 on 140307 degrees of freedom
Residual deviance: 120790 on 140251 degrees of freedom
(109662 observations deleted due to missingness)
AIC: 120904
Number of Fisher Scoring iterations: 5
# Extracting Odds Ratios (ORs) and Confidence Intervals (CIs) for the unadjusted model <- exp (prepreg_unadjusted_results$ coefficients[, "Estimate" ])<- prepreg_unadjusted_results$ coefficients[, "Std. Error" ]<- exp (confint.default (logit_prepreg_unadjusted))# Extracting Odds Ratios (ORs) and Confidence Intervals (CIs) for the adjusted model <- exp (prepreg_adjusted_results$ coefficients[, "Estimate" ])<- prepreg_adjusted_results$ coefficients[, "Std. Error" ]<- exp (confint.default (logit_prepreg_adjusted))# Combine ORs and CIs into data frames <- data.frame (OR = prepreg_unadjusted_or,CI_lower = prepreg_unadjusted_ci[, 1 ],CI_upper = prepreg_unadjusted_ci[, 2 ]<- data.frame (OR = prepreg_adjusted_or,CI_lower = prepreg_adjusted_ci[, 1 ],CI_upper = prepreg_adjusted_ci[, 2 ]# Print results print (prepreg_unadjusted_df)
OR CI_lower CI_upper
(Intercept) 0.2687213 0.2648683 0.2726302
PRE_VIT_RAW1 1.0558990 1.0310962 1.0812983
print (prepreg_adjusted_df)
OR CI_lower CI_upper
(Intercept) 0.1124954 0.09349452 0.1353579
PRE_VIT_RAW1 1.1443421 1.11003093 1.1797138
MAT_AGE_PU 1.0374012 1.03418447 1.0406280
MOM_BMI 1.0031084 1.00101250 1.0052087
PRE_LB_NAPHSIS1 0.6451379 0.62336024 0.6676764
PRE_LB_NAPHSIS2 0.6378828 0.61338679 0.6633572
MAT_ED<= 12th grade, no diploma 1.4397337 1.34800593 1.5377033
MAT_EDHigh school graduate / GED 1.3964404 1.33178769 1.4642318
MAT_EDSome college, no degree 1.3037556 1.25295525 1.3566157
STATEAR 5.9424325 5.01269542 7.0446139
STATEAZ 0.6346308 0.45486281 0.8854454
STATECO 2.0907073 1.77305737 2.4652655
STATEDC 4.7522100 3.94893108 5.7188892
STATEDE 3.7429471 3.17350469 4.4145683
STATEGA 3.4304040 2.85897056 4.1160521
STATEIA 0.9845857 0.79492536 1.2194968
STATEIL 4.4568528 3.79123623 5.2393298
STATEIN 1.3933981 1.01931232 1.9047728
STATEKS 9.7415024 8.30254461 11.4298535
STATEKY 1.0526654 0.85617581 1.2942487
STATELA 1.3122629 1.08774789 1.5831185
STATEMA 0.8408291 0.70576598 1.0017393
STATEMD 6.5903991 5.58879940 7.7715010
STATEME 8.8292311 7.51413700 10.3744876
STATEMI 3.0186616 2.57294841 3.5415860
STATEMN 0.8930333 0.71824071 1.1103638
STATEMO 4.8489882 4.12466818 5.7005037
STATEMP 0.9226598 0.41231322 2.0646951
STATEMS 9.8908160 8.35229662 11.7127355
STATEMT 0.9354937 0.76375278 1.1458530
STATEND 1.0333249 0.83009699 1.2863080
STATENE 0.8815547 0.73024405 1.0642178
STATENH 9.0496742 7.63082153 10.7323442
STATENJ 0.9183293 0.76049099 1.1089266
STATENM 0.9163742 0.75970660 1.1053499
STATENY 9.5934286 8.15186660 11.2899139
STATEOR 0.8184794 0.67478777 0.9927691
STATEPA 5.8796518 5.00838758 6.9024821
STATEPR 10.0997374 8.58182769 11.8861272
STATERI 6.7381133 5.68328104 7.9887252
STATESD 0.8088317 0.65867549 0.9932186
STATETN 10.2074001 8.42492224 12.3670004
STATEUT 6.4260558 5.49118865 7.5200828
STATEVA 1.4066069 1.17485412 1.6840754
STATEWA 0.7453778 0.61454428 0.9040652
STATEWI 1.1366539 0.95043042 1.3593653
STATEWV 9.2187674 7.71870905 11.0103480
STATEWY 6.6879679 5.62144806 7.9568315
STATEYC 4.0621465 3.45870748 4.7708672
INCOME8High income ($85,001 or more) 0.7213821 0.68881609 0.7554878
INCOME8Middle ($40,001 - $85,000) 0.8075703 0.77584247 0.8405956
PNC_VST 0.8875870 0.88407796 0.8911099
MAT_RACE_PUAmerican Indian 0.6433131 0.56730218 0.7295084
MAT_RACE_PUAsian 1.4603693 1.36715760 1.5599362
MAT_RACE_PUBlack 1.4731501 1.41156299 1.5374243
MAT_RACE_PUHawaiian/Other Pacific Islander 0.7647852 0.53690067 1.0893940
MAT_RACE_PUOther/Multiple Race 1.0727278 1.01156746 1.1375860
Pre-pregnancy folic acid intake is associated with higher odds of LBW (OR = 1.056, p-value = 7.29e-06, 95% CI: [1.031, 1.081]).
Adjusting for confounders, pre-pregnancy folic acid intake is associated with higher odds of LBW (OR = 1.144, p-value = < 2e-16, 95% CI: [1.110, 1.180]).
During Preg
# Unadjusted logistic regression for LBW with during-pregnancy folic acid <- glm (~ VPP_VIT_RAW,data = data_clean,family = binomial ()# Adjusted logistic regression for LBW with during-pregnancy folic acid <- glm (~ VPP_VIT_RAW + MAT_AGE_PU + MOM_BMI + PRE_LB_NAPHSIS + MAT_ED + STATE + INCOME8 + PNC_VST + MAT_RACE_PU,data = data_clean,family = binomial ()# Display unadjusted and adjusted results for during-pregnancy folic acid <- summary (logit_duringpreg_unadjusted, exponentiate = TRUE , conf.int = TRUE )<- summary (logit_duringpreg_adjusted, exponentiate = TRUE , conf.int = TRUE )print (duringpreg_unadjusted_results)
Call:
glm(formula = LBW ~ VPP_VIT_RAW, family = binomial(), data = data_clean)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.289061 0.007822 -164.79 < 2e-16 ***
VPP_VIT_RAW1 -0.081818 0.010599 -7.72 1.17e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 222773 on 217578 degrees of freedom
Residual deviance: 222714 on 217577 degrees of freedom
(32391 observations deleted due to missingness)
AIC: 222718
Number of Fisher Scoring iterations: 4
print (duringpreg_adjusted_results)
Call:
glm(formula = LBW ~ VPP_VIT_RAW + MAT_AGE_PU + MOM_BMI + PRE_LB_NAPHSIS +
MAT_ED + STATE + INCOME8 + PNC_VST + MAT_RACE_PU, family = binomial(),
data = data_clean)
Coefficients:
Estimate Std. Error z value
(Intercept) -2.0502920 0.0795872 -25.762
VPP_VIT_RAW1 -0.0404300 0.0131530 -3.074
MAT_AGE_PU 0.0372128 0.0014233 26.145
MOM_BMI 0.0009019 0.0009647 0.935
PRE_LB_NAPHSIS1 -0.4364337 0.0158473 -27.540
PRE_LB_NAPHSIS2 -0.4674789 0.0180348 -25.921
MAT_ED<= 12th grade, no diploma 0.3230560 0.0292431 11.047
MAT_EDHigh school graduate / GED 0.3042536 0.0216124 14.078
MAT_EDSome college, no degree 0.2280738 0.0186694 12.216
STATEAR 1.7249889 0.0713687 24.170
STATEAZ -0.2877186 0.1367168 -2.104
STATECO 0.6113754 0.0704209 8.682
STATEDC 1.5247445 0.0805698 18.925
STATEDE 1.2780424 0.0699303 18.276
STATEGA 1.2520713 0.0751488 16.661
STATEIA -0.0978861 0.0914388 -1.071
STATEIL 1.4297408 0.0680872 20.999
STATEIN 0.1768309 0.1453115 1.217
STATEKS 2.2347607 0.0670807 33.315
STATEKY 0.0399962 0.0899621 0.445
STATELA 0.2248861 0.0809273 2.779
STATEMA -0.2164029 0.0749523 -2.887
STATEMD 1.7501006 0.0700324 24.990
STATEME 2.1100175 0.0682377 30.922
STATEMI 1.0442665 0.0670591 15.572
STATEMN -0.1910784 0.0991522 -1.927
STATEMO 1.5115553 0.0680361 22.217
STATEMP 0.2357147 0.3346011 0.704
STATEMS 2.2682398 0.0703985 32.220
STATEMT -0.1525614 0.0893948 -1.707
STATEND -0.1464080 0.0993348 -1.474
STATENE -0.1008937 0.0782717 -1.289
STATENH 2.1303596 0.0736822 28.913
STATENJ -0.1759480 0.0811353 -2.169
STATENM -0.1390056 0.0790530 -1.758
STATENY 2.2122773 0.0692596 31.942
STATEOR -0.2707184 0.0827326 -3.272
STATEPA 1.7142948 0.0677445 25.305
STATEPR 2.2966456 0.0691217 33.226
STATERI 1.8311811 0.0726180 25.217
STATESD -0.2875154 0.0881165 -3.263
STATETN 2.2312645 0.0821361 27.165
STATEUT 1.7445963 0.0659029 26.472
STATEVA 0.3083395 0.0766748 4.021
STATEWA -0.4028504 0.0831470 -4.845
STATEWI 0.0638199 0.0769775 0.829
STATEWV 2.1374543 0.0748461 28.558
STATEWY 1.8282111 0.0737523 24.789
STATEYC 1.3593150 0.0676092 20.105
INCOME8High income ($85,001 or more) -0.2568979 0.0214039 -12.002
INCOME8Middle ($40,001 - $85,000) -0.1655124 0.0182499 -9.069
PNC_VST -0.1192813 0.0018294 -65.201
MAT_RACE_PUAmerican Indian -0.3521376 0.0557625 -6.315
MAT_RACE_PUAsian 0.3590648 0.0295478 12.152
MAT_RACE_PUBlack 0.3900052 0.0193333 20.173
MAT_RACE_PUHawaiian/Other Pacific Islander -0.2715430 0.1511063 -1.797
MAT_RACE_PUOther/Multiple Race 0.0446723 0.0262292 1.703
Pr(>|z|)
(Intercept) < 2e-16 ***
VPP_VIT_RAW1 0.00211 **
MAT_AGE_PU < 2e-16 ***
MOM_BMI 0.34987
PRE_LB_NAPHSIS1 < 2e-16 ***
PRE_LB_NAPHSIS2 < 2e-16 ***
MAT_ED<= 12th grade, no diploma < 2e-16 ***
MAT_EDHigh school graduate / GED < 2e-16 ***
MAT_EDSome college, no degree < 2e-16 ***
STATEAR < 2e-16 ***
STATEAZ 0.03534 *
STATECO < 2e-16 ***
STATEDC < 2e-16 ***
STATEDE < 2e-16 ***
STATEGA < 2e-16 ***
STATEIA 0.28439
STATEIL < 2e-16 ***
STATEIN 0.22364
STATEKS < 2e-16 ***
STATEKY 0.65662
STATELA 0.00545 **
STATEMA 0.00389 **
STATEMD < 2e-16 ***
STATEME < 2e-16 ***
STATEMI < 2e-16 ***
STATEMN 0.05396 .
STATEMO < 2e-16 ***
STATEMP 0.48114
STATEMS < 2e-16 ***
STATEMT 0.08790 .
STATEND 0.14051
STATENE 0.19739
STATENH < 2e-16 ***
STATENJ 0.03012 *
STATENM 0.07868 .
STATENY < 2e-16 ***
STATEOR 0.00107 **
STATEPA < 2e-16 ***
STATEPR < 2e-16 ***
STATERI < 2e-16 ***
STATESD 0.00110 **
STATETN < 2e-16 ***
STATEUT < 2e-16 ***
STATEVA 5.79e-05 ***
STATEWA 1.27e-06 ***
STATEWI 0.40706
STATEWV < 2e-16 ***
STATEWY < 2e-16 ***
STATEYC < 2e-16 ***
INCOME8High income ($85,001 or more) < 2e-16 ***
INCOME8Middle ($40,001 - $85,000) < 2e-16 ***
PNC_VST < 2e-16 ***
MAT_RACE_PUAmerican Indian 2.70e-10 ***
MAT_RACE_PUAsian < 2e-16 ***
MAT_RACE_PUBlack < 2e-16 ***
MAT_RACE_PUHawaiian/Other Pacific Islander 0.07233 .
MAT_RACE_PUOther/Multiple Race 0.08854 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 175389 on 174316 degrees of freedom
Residual deviance: 148235 on 174260 degrees of freedom
(75653 observations deleted due to missingness)
AIC: 148349
Number of Fisher Scoring iterations: 5
# Extracting Odds Ratios (ORs) and Confidence Intervals (CIs) for the unadjusted model <- exp (duringpreg_unadjusted_results$ coefficients[, "Estimate" ])<- duringpreg_unadjusted_results$ coefficients[, "Std. Error" ]<- exp (confint.default (logit_duringpreg_unadjusted))# Extracting Odds Ratios (ORs) and Confidence Intervals (CIs) for the adjusted model <- exp (duringpreg_adjusted_results$ coefficients[, "Estimate" ])<- duringpreg_adjusted_results$ coefficients[, "Std. Error" ]<- exp (confint.default (logit_duringpreg_adjusted))# Combine ORs and CIs into data frames <- data.frame (OR = duringpreg_unadjusted_or,CI_lower = duringpreg_unadjusted_ci[, 1 ],CI_upper = duringpreg_unadjusted_ci[, 2 ]<- data.frame (OR = duringpreg_adjusted_or,CI_lower = duringpreg_adjusted_ci[, 1 ],CI_upper = duringpreg_adjusted_ci[, 2 ]# Print results print (duringpreg_unadjusted_df)
OR CI_lower CI_upper
(Intercept) 0.2755294 0.2713373 0.2797862
VPP_VIT_RAW1 0.9214394 0.9024954 0.9407810
print (duringpreg_adjusted_df)
OR CI_lower CI_upper
(Intercept) 0.1286973 0.1101095 0.1504230
VPP_VIT_RAW1 0.9603764 0.9359348 0.9854562
MAT_AGE_PU 1.0379139 1.0350225 1.0408134
MOM_BMI 1.0009023 0.9990115 1.0027966
PRE_LB_NAPHSIS1 0.6463373 0.6265705 0.6667277
PRE_LB_NAPHSIS2 0.6265799 0.6048187 0.6491241
MAT_ED<= 12th grade, no diploma 1.3813427 1.3043967 1.4628277
MAT_EDHigh school graduate / GED 1.3556128 1.2993888 1.4142695
MAT_EDSome college, no degree 1.2561781 1.2110437 1.3029946
STATEAR 5.6124586 4.8798219 6.4550906
STATEAZ 0.7499726 0.5736828 0.9804353
STATECO 1.8429645 1.6053680 2.1157255
STATEDC 4.5939696 3.9228976 5.3798389
STATEDE 3.5896058 3.1298387 4.1169119
STATEGA 3.4975799 3.0185675 4.0526061
STATEIA 0.9067522 0.7579766 1.0847294
STATEIL 4.1776164 3.6557175 4.7740228
STATEIN 1.1934293 0.8976506 1.5866679
STATEKS 9.3442454 8.1930399 10.6572070
STATEKY 1.0408069 0.8725579 1.2414980
STATELA 1.2521801 1.0685168 1.4674127
STATEMA 0.8054107 0.6953732 0.9328609
STATEMD 5.7551815 5.0170374 6.6019269
STATEME 8.2483856 7.2158066 9.4287262
STATEMI 2.8413137 2.4913708 3.2404102
STATEMN 0.8260678 0.6801696 1.0032616
STATEMO 4.5337767 3.9677813 5.1805101
STATEMP 1.2658131 0.6569867 2.4388357
STATEMS 9.6623781 8.4170664 11.0919345
STATEMT 0.8585062 0.7205273 1.0229076
STATEND 0.8638052 0.7109873 1.0494694
STATENE 0.9040291 0.7754565 1.0539194
STATENH 8.4178936 7.2859305 9.7257218
STATENJ 0.8386616 0.7153593 0.9832168
STATENM 0.8702231 0.7453162 1.0160631
STATENY 9.1364990 7.9767492 10.4648662
STATEOR 0.7628313 0.6486440 0.8971201
STATEPA 5.5527583 4.8623313 6.3412224
STATEPR 9.9407813 8.6812848 11.3830077
STATERI 6.2412537 5.4132651 7.1958878
STATESD 0.7501250 0.6311445 0.8915353
STATETN 9.3116330 7.9270475 10.9380584
STATEUT 5.7235906 5.0300459 6.5127616
STATEVA 1.3611630 1.1712361 1.5818883
STATEWA 0.6684121 0.5678969 0.7867181
STATEWI 1.0659004 0.9166283 1.2394814
STATEWV 8.4778285 7.3210855 9.8173386
STATEWY 6.2227447 5.3852259 7.1905156
STATEYC 3.8935253 3.4103107 4.4452076
INCOME8High income ($85,001 or more) 0.7734472 0.7416716 0.8065841
INCOME8Middle ($40,001 - $85,000) 0.8474593 0.8176821 0.8783209
PNC_VST 0.8875581 0.8843813 0.8907463
MAT_RACE_PUAmerican Indian 0.7031833 0.6303815 0.7843929
MAT_RACE_PUAsian 1.4319896 1.3514150 1.5173682
MAT_RACE_PUBlack 1.4769885 1.4220686 1.5340294
MAT_RACE_PUHawaiian/Other Pacific Islander 0.7622025 0.5668243 1.0249256
MAT_RACE_PUOther/Multiple Race 1.0456851 0.9932866 1.1008478
Pregnancy folic acid intake is associated with lower odds of LBW (OR = 0.9214, p-value = 1.17e-14, 95% CI: [0.9025, 0.9408]).
Adjusting for confounders, there was a slight increase in odds but pregnancy folic acid intake is still associated with lower odds of LBW (OR = 0.9603, p-value = < 2e-16, 95% CI: [0.9359, 0.9855]).
Forrest Plot Logistic Regression Results
# Combine unadjusted and adjusted results for PRE_VIT_RAW <- data.frame (Exposure = c ("Pre-Pregnancy Folic Usage (Unadj)" , "Pre-Pregnancy Folic Usage (Adj)" ),OR = c (prepreg_unadjusted_or["PRE_VIT_RAW1" ], prepreg_adjusted_or["PRE_VIT_RAW1" ]),CI_lower = c (prepreg_unadjusted_ci["PRE_VIT_RAW1" , 1 ], prepreg_adjusted_ci["PRE_VIT_RAW1" , 1 ]),CI_upper = c (prepreg_unadjusted_ci["PRE_VIT_RAW1" , 2 ], prepreg_adjusted_ci["PRE_VIT_RAW1" , 2 ]),Model = c ("Unadjusted" , "Adjusted" )# Combine unadjusted and adjusted results for VPP_VIT_RAW <- data.frame (Exposure = c ("Pregnancy Folic Usage (Unadj)" , "Pregnancy Folic Usage (Adj)" ),OR = c (duringpreg_unadjusted_or["VPP_VIT_RAW1" ], duringpreg_adjusted_or["VPP_VIT_RAW1" ]),CI_lower = c (duringpreg_unadjusted_ci["VPP_VIT_RAW1" , 1 ], duringpreg_adjusted_ci["VPP_VIT_RAW1" , 1 ]),CI_upper = c (duringpreg_unadjusted_ci["VPP_VIT_RAW1" , 2 ], duringpreg_adjusted_ci["VPP_VIT_RAW1" , 2 ]),Model = c ("Unadjusted" , "Adjusted" )# Combine both data frames into one <- bind_rows (prepreg_df, duringpreg_df)
Forrest Plot PrePregnancy
<- prepreg_df %>% mutate (Exposure = factor (levels = c ("Pre-Pregnancy Folic Usage (Unadj)" , "Pre-Pregnancy Folic Usage (Adj)" )Model = factor (Model, levels = c ("Unadjusted" , "Adjusted" )) # Ensure the desired legend order # Plot for Pre-Pregnancy Folic Usage ggplot (prepreg_df, aes (x = Exposure, y = OR, color = Model, group = Exposure)) + geom_point (size = 3 ) + geom_errorbar (aes (ymin = CI_lower, ymax = CI_upper), width = 0.2 ) + scale_y_log10 () + # Log scale for better visualization labs (x = "Exposure Variables" ,y = "Odds Ratio (95% CI)" ,title = "Odds Ratios with 95% Confidence Intervals for Pre-Pregnancy Folic Usage" ,color = "Model Type" # Legend title + theme_minimal () + theme (axis.text.x = element_text (angle = 45 , hjust = 1 )) + scale_color_manual (values = c ("Unadjusted" = "blue" , "Adjusted" = "red" )) # Manual color mapping
Forrest Plot Pregnancy
<- duringpreg_df %>% mutate (Exposure = factor (levels = c ("Pregnancy Folic Usage (Unadj)" , "Pregnancy Folic Usage (Adj)" )Model = factor (Model, levels = c ("Unadjusted" , "Adjusted" )) # Ensure the desired legend order # Plot for During Pregnancy Folic Usage ggplot (duringpreg_df, aes (x = Exposure, y = OR, color = Model, group = Exposure)) + geom_point (size = 3 ) + geom_errorbar (aes (ymin = CI_lower, ymax = CI_upper), width = 0.2 ) + scale_y_log10 () + # Log scale for better visualization labs (x = "Exposure Variables" ,y = "Odds Ratio (95% CI)" ,title = "Odds Ratios with 95% Confidence Intervals for During Pregnancy Folic Usage" ,color = "Model Type" # Legend title + theme_minimal () + theme (axis.text.x = element_text (angle = 45 , hjust = 1 )) + scale_color_manual (values = c ("Unadjusted" = "blue" , "Adjusted" = "red" )) # Manual color mapping
Forrest Plot Combined Results
<- combined_df %>% mutate (Exposure = factor (levels = c ("Pre-Pregnancy Folic Usage (Unadj)" , "Pre-Pregnancy Folic Usage (Adj)" , "Pregnancy Folic Usage (Unadj)" , "Pregnancy Folic Usage (Adj)" Model = factor (Model, levels = c ("Unadjusted" , "Adjusted" )) # Ensure desired legend order # Plot the Odds Ratios with Confidence Intervals - combined intake ggplot (combined_df, aes (x = Exposure, y = OR, color = Model, group = Exposure)) + geom_point (size = 3 ) + geom_errorbar (aes (ymin = CI_lower, ymax = CI_upper), width = 0.2 ) + scale_y_log10 () + # Log scale for better visualization labs (x = "Exposure Variables" ,y = "Odds Ratio (95% CI)" ,title = "Odds Ratios with 95% Confidence Intervals for Pre-Pregnancy and Pregnancy Folic Usage" ,color = "Model Type" # Legend title + theme_minimal () + theme (axis.text.x = element_text (angle = 60 , hjust = 1 )) + scale_color_manual (values = c ("Unadjusted" = "blue" , "Adjusted" = "red" )) # Manual color mapping
Pre-pregnancy folic acid intake is associated with higher odds of LBW (OR = 1.056, p-value = 7.29e-06, 95% CI: [1.031, 1.081]).
Adjusting for confounders, pre-pregnancy folic acid intake is associated with higher odds of LBW (OR = 1.144, p-value = < 2e-16, 95% CI: [1.110, 1.180]).
Pregnancy folic acid intake is associated with lower odds of LBW (OR = 0.9214, p-value = 1.17e-14, 95% CI: [0.9025, 0.9408]).
Adjusting for confounders, there was a slight increase in odds but pregnancy folic acid intake is still associated with lower odds of LBW (OR = 0.96038, p-value = < 2e-16, 95% CI: [0.9359, 0.9855]).
Limitations
This analysis has several key limitations that should be considered when interpreting the findings. A major concern is the potential for confounding, particularly in the observed higher odds for pre-pregnancy folic acid intake and LBW. Women who take folic acid before pregnancy may have underlying health complications or a history of adverse pregnancy outcomes that motivated supplementation. These conditions, rather than the folic acid intake itself, could increase the likelihood of LBW, confounding the observed relationship. Additionally, social desirability bias may have influenced the results, as participants might have overreported their folic acid use due to its widely recognized health benefits. This bias could lead to an overestimation of the association between folic acid intake and LBW, as some participants may have claimed use without actually adhering to supplementation. Furthermore, the binary yes/no question about folic acid intake fails to capture critical information, such as the frequency, dosage, or timing of supplementation. This limitation reduces the ability to assess whether intake was sufficient to achieve effective outcomes, potentially obscuring more nuanced relationships. Together, these limitations underscore the importance of cautious interpretation and highlight the need for more detailed data collection and rigorous confounder adjustment in future research.
Conclusion
This study highlights several critical factors that influence LBW outcomes, with a particular focus on maternal folic acid intake, maternal education, socioeconomic status, and race. The analysis suggests that both pre-pregnancy and pregnancy folic acid intake are significantly associated with LBW, although the association with pregnancy folic acid intake appears to be protective (OR = 0.96), while pre-pregnancy intake is linked to a slight increase in the odds of LBW (OR = 1.14). These findings suggest that the timing of folic acid supplementation may play a role in birth weight outcomes, with pregnancy supplementation potentially offering more favorable outcomes.
Socioeconomic factors, including income and maternal education, are also significant contributors to LBW risk. Higher maternal education and income were observed to have lower rates of LBW, emphasizing the importance of addressing socioeconomic disparities in maternal health. The findings further indicate that racial disparities exist, with Black women having the highest proportion of LBW cases (26.1%), followed by White and Asian women, underscoring the need for targeted interventions within different racial groups.
Maternal age, parity, and prenatal care visits were found to influence birth weight outcomes as well. First-time pregnancies were associated with the highest LBW risk, suggesting that a history of prior pregnancies may improve maternal health outcomes. Additionally, more frequent prenatal care visits were linked to better birth weight outcomes, underscoring the importance of adequate prenatal care in reducing LBW risk.
This study also identifies geographical disparities in LBW, with certain states and territories, such as Kansas and Puerto Rico, showing higher LBW counts, potentially reflecting regional variations in healthcare access and maternal health services.
In conclusion, improving maternal health outcomes and reducing LBW rates require a multifaceted approach that includes addressing nutritional, socioeconomic, healthcare access, and racial disparities. The role of folic acid intake, particularly during pregnancy, should be emphasized in maternal health guidelines, and public health interventions should focus on increasing access to prenatal care, promoting education, and addressing the underlying socioeconomic and racial factors that contribute to LBW.
References
Organization WH. International statistical classification of diseases and related health problems, tenth revision, 2nd ed. World Health Organization; 2004.
Jańczewska, I., Wierzba, J., Jańczewska, A., Szczurek-Gierczak, M., & Domżalska-Popadiuk, I. (2023). Prematurity and Low Birth Weight and Their Impact on Childhood Growth Patterns and the Risk of Long-Term Cardiovascular Sequelae. Children , 10 (10), Article 10. https://doi.org/10.3390/children10101599
Fu, M., Song, W., Yu, G., Yu, Y., & Yang, Q. (2023). Risk factors for length of NICU stay of newborns: A systematic review. Frontiers in Pediatrics , 11 , 1121406. https://doi.org/10.3389/fped.2023.1121406
Houk, V. N., Oakley, G. P., Erickson, J. D., Mulinare, J., & James, L. M. (n.d.). Recommendations for the use of folic acid to reduce the number of cases of spina bifida and other neural tube defects . Retrieved November 12, 2024, from https://stacks.cdc.gov/view/cdc/7079
Scholl, T. O., & Johnson, W. G. (2000). Folic acid: Influence on the outcome of pregnancy1234. The American Journal of Clinical Nutrition , 71 (5), 1295S-1303S. https://doi.org/10.1093/ajcn/71.5.1295s
